detection of defects in polyurethane fabrics based on ... · abstract school of engineering...

MASTER THESIS

Detection of defects in polyurethanefabrics based on computer vision

techniques.

Author:

Rafael Angel Villegas

Miranda

Director:

Dr. Hernan Darıo Benıtez

Restrepo.

Department of Electronics and Computer Science

February 2015

Abstract

School of Engineering

Department of Electronics and Computer Science

Master of Engineering

Detection of defects in polyurethane fabrics based on computer vision

techniques.

by Rafael Angel Villegas Miranda

Quality inspection is one of the most important aspects of modern industrial manufac-

turing of textiles. Nonetheless, at some companies this inspection is manual, expensive,

and oversights defects in textiles. These issues have a negative impact on companies’

productivity. The purpose of this applied research project is to detect the most costly

defects that occur in fabrics made from polyurethane, by using computer vision and

analysis of digital images obtained in controlled environments. The objects of interest

in these images correspond to cloth cuttings used to manufacture car chairs. There are

two topics to be addressed in this work. These are the automatic classification of cloths

and detection of defects in fabrics. Genetic algorithms find the optimal parameters in

Gabor filter banks. Receiving operating curve (ROC) and area under curve (AUC) are

the figures of merit used for evaluate the performance of the proposed system with re-

spect to manually segmented images. Results show that the computer based systems

accomplishes true positive rates greater than 90% for the inspected fabric types.

Keywords: computer vision, automated vision system, textiles, digital image processing,

fabric defects, textile fabrics.

Acknowledgements

The author expresses their acknowledgments to:

Hernan Darıo Benıtez Restrepo, PhD in Engineering and thesis Director, for his dedi-

cation and excellent guidance.

Albeiro Aponte Vargas, for his time and permission to use the front base of the calibra-

tion process.

vi

Contents

Abstract v

Acknowledgements vi

Contents vii

List of Figures x

List of Tables xii

Abbreviations xiii

1 INTRODUCTION 1

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Description of the Research Problem . . . . . . . . . . . . . . . . . . . . . 2

1.3 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.4 General Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.5 Specific Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.6 Project Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.7 Expected Project Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 THEORETICAL FRAMEWORK 5

2.1 Previous works and theoretical background. . . . . . . . . . . . . . . . . . 5

2.1.1 Statistical Approaches . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.1.2 Spectral Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.1.3 Model-Based Approaches . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 Camera Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.2.1 Image Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2.2 Homogeneous coordinates . . . . . . . . . . . . . . . . . . . . . . . 13

2.2.3 Perspective projection . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.2.4 Homogeneous transformation . . . . . . . . . . . . . . . . . . . . . 16

2.2.5 Camera parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.2.5.1 Intrinsic parameters . . . . . . . . . . . . . . . . . . . . . 18

2.2.5.2 Extrinsic parameters . . . . . . . . . . . . . . . . . . . . . 19

2.2.6 Lens distortions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.2.6.1 Radial distortion . . . . . . . . . . . . . . . . . . . . . . . 20

2.2.6.2 Tangential distortion . . . . . . . . . . . . . . . . . . . . 21

vii

Contents viii

2.3 Illumination systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.4 Fabrics and selection of defects . . . . . . . . . . . . . . . . . . . . . . . . 23

2.4.1 Main defects ranking . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.4.2 Defects criteria selection . . . . . . . . . . . . . . . . . . . . . . . . 25

3 DEFECT DETECTION AND FABRICS REPRESENTATION 29

3.1 Defect detection in fabrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.1.1 Gabor filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.1.1.1 Gabor filter selection method . . . . . . . . . . . . . . . . 33

3.1.1.2 Defect detection . . . . . . . . . . . . . . . . . . . . . . . 34

3.1.1.3 Gabor filter parameters . . . . . . . . . . . . . . . . . . . 34

3.2 Feature extraction for fabrics representation . . . . . . . . . . . . . . . . . 35

3.2.1 Fabric recognition based on minimum distance classifiers . . . . . . 37

3.2.2 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.2.3 Training and validation data sets . . . . . . . . . . . . . . . . . . . 39

3.2.4 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.2.5 Simple thresholding . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4 RESULTS AND ANALYSIS 44

4.1 Description of the proposed system . . . . . . . . . . . . . . . . . . . . . . 44

4.2 Camera Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.2.1 Experimental set-up . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.2.2 Results - comparison manual method . . . . . . . . . . . . . . . . . 49

4.2.3 Experiments and results of calibration process. . . . . . . . . . . . 50

4.2.3.1 Parameters of the function CalibrateCamera (OpenCV) . 52

4.2.3.2 Re-projection error estimation . . . . . . . . . . . . . . . 53

4.2.4 Evaluation of inspection methods . . . . . . . . . . . . . . . . . . . 57

4.2.5 Evaluation procedure . . . . . . . . . . . . . . . . . . . . . . . . . 58

4.3 Parameter tuning for Gabor filter banks and simple thresholding approach 59

4.3.1 Gabor filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

4.3.2 Simple thresholding . . . . . . . . . . . . . . . . . . . . . . . . . . 65

4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.4.1 Analysis of results . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.4.1.1 Gabor filters method . . . . . . . . . . . . . . . . . . . . 67

4.4.1.2 Simple thresholding . . . . . . . . . . . . . . . . . . . . . 68

5 CONCLUSIONS AND FUTURE WORK 75

5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

A Front system 77

B Prototype (C.E.) 80

C Developer Guide 82

Contents ix

D User guide 85

Bibliography 90

List of Figures

2.1 Point Grey - Flea3 FL3-GE-20S4C-C Camera. . . . . . . . . . . . . . . . . 11

2.2 Edmunds Optics - 6 mm Compact Fixed Focal Length Lens. . . . . . . . . 12

2.3 Pinhole model with identification of key variables . . . . . . . . . . . . . . 12

2.4 Modeling perspective projection [1] . . . . . . . . . . . . . . . . . . . . . . 14

2.5 Relationship between world and image coordinates. . . . . . . . . . . . . . 14

2.6 Final position (translation and rotation) . . . . . . . . . . . . . . . . . . . 16

2.7 Order of applying parameters from the real world to the camera . . . . . 20

2.8 Example of radial distortion (barrel distortion). [2] . . . . . . . . . . . . . 20

2.9 Tubular bulb, T8 led 22W - 1.20 m. . . . . . . . . . . . . . . . . . . . . . 23

2.10 Selected fabric images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.11 Selected images of defective fabrics. . . . . . . . . . . . . . . . . . . . . . . 28

3.1 Components of a Gabor function in spatial domain. . . . . . . . . . . . . 31

3.2 Gabor filter parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.3 Features extraction from training set. . . . . . . . . . . . . . . . . . . . . 37

3.4 Features extraction process diagram with test set (Individual). . . . . . . 38

3.5 Mean graph - karyatide fabric (75 images). . . . . . . . . . . . . . . . . . 40

3.6 Variance graph - kariatyde fabric (75 images). . . . . . . . . . . . . . . . . 41

3.7 Kurtosis graph - kariatyde fabric (75 images). . . . . . . . . . . . . . . . . 41

3.8 Skewness graph - kariatyde fabric (75 images). . . . . . . . . . . . . . . . 42

3.9 Final graph representation for five fabrics types (75 images by fabric). . . 43

4.1 General process diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

4.2 Geometric projections - pinhole model. . . . . . . . . . . . . . . . . . . . . 46

4.3 a) Area calculated by trigonometric projections - Pinhole model. b) Ef-fective area with an additional margin of 5% for lighting. . . . . . . . . . 48

4.4 Inspection areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4.5 Proposed prototype from a front view. . . . . . . . . . . . . . . . . . . . . 49

4.6 Markers on the table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.7 Chessboard calibration pattern 5 x 4. . . . . . . . . . . . . . . . . . . . . . 51

4.8 Parameters for Calibration Process - (Flea3 FL3-GE-20S4C-C Color GigECamera). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

4.9 Sample images for the calibration process . . . . . . . . . . . . . . . . . . 54

4.10 Identification of corners during the calibration process . . . . . . . . . . . 54

4.11 Values of the parameters obtained in the calibration process . . . . . . . . 55

4.12 Shapes selecting for testing. . . . . . . . . . . . . . . . . . . . . . . . . . . 55

4.13 Manual measurement of circle diameter D=120 mm. . . . . . . . . . . . . 56

4.14 ROC curve distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

x

List of Figures xi

4.15 Kariatyde fabric defect and manual segmentation . . . . . . . . . . . . . . 58

4.16 ROC curves (individuals) for petit point gray fabric. . . . . . . . . . . . . 59

4.17 AUC curve for images of fabric petit point red fabric (mean = 0.90876,standard deviation = 0.02488). . . . . . . . . . . . . . . . . . . . . . . . . 60

4.18 Simple thresholding result and manual segmentation. . . . . . . . . . . . . 60

4.19 Diagram process - Gabor filters method . . . . . . . . . . . . . . . . . . . 61

4.20 Diagram process - simple thresholding method . . . . . . . . . . . . . . . 62

4.21 a)angle=0.00, frequency=0.125, α=2.449, σx=3.567, σy=0.433; b) an-gle=0.0, frequency=0.306, α=2.449, σx=1.456, σy=0.439; c) angle=0.0,frequency=0.750, α=2.449, σx=0.594, σy=0.476 . . . . . . . . . . . . . . . 63



4.24 Diagram process - genetic algorithm. . . . . . . . . . . . . . . . . . . . . . 66

4.25 Inspection process results - Kariatyde fabric (training set). . . . . . . . . . 69

4.26 Inspection process results - Kariatyde fabric (test set). . . . . . . . . . . . 69

4.27 Inspection process results - Vulcron vinyl W/L fabric (training set). . . . 70

4.28 Inspection process results - Vulcron vinyl W/L fabric (test set). . . . . . . 70

4.29 Inspection process results - Vulcron vinyl N/L fabric (training set). . . . . 71

4.30 Inspection process results - Vulcron vinyl N/L fabric (test set). . . . . . . 71

4.31 Inspection process results - petit point gray fabric (training set). . . . . . 72

4.32 Inspection process results - petit point gray fabric (test set). . . . . . . . . 72

4.33 Inspection process results - petit point red fabric (training set). . . . . . . 73

4.34 Inspection process results - petit point red fabric (test set). . . . . . . . . 73

A.1 Calibrate Camera option. . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

A.2 Defect inspection option. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

A.3 Representation (Identification fabric type) option. . . . . . . . . . . . . . 79

C.1 Entity-relation model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

C.2 Chessboard pattern - 9x7 squares. . . . . . . . . . . . . . . . . . . . . . . 84

D.1 Use case camera calibrate . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

D.2 Use case fabric representation . . . . . . . . . . . . . . . . . . . . . . . . . 87

D.3 Use case Inspection (fabric defect detection process) . . . . . . . . . . . . 88

D.4 Use case Inspection (fabric defect detection process) . . . . . . . . . . . . 89

List of Tables

2.1 Fabric defect detection methods. . . . . . . . . . . . . . . . . . . . . . . . 10

2.2 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3 Camera specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.4 Preferred sustained luminance levels for different locations or visual tasks- Encyclopedia of health and safety at work [3]. . . . . . . . . . . . . . . . 22

2.5 Information provided by the manufacturer of T8 LED Tube 22W. . . . . . 23

2.6 Ranking of the main defects on fabrics occurred between January andSeptember 2013 detected by human inspection . . . . . . . . . . . . . . . 25

2.7 Main defects selected under the criterion of the highest cost . . . . . . . . 26

3.1 Statistical features for fabric representation . . . . . . . . . . . . . . . . . 40

3.2 Confusion matrix for test identification fabrics (rows are the true class) . 40

4.1 Values obtained by geometry - pinhole model (meters). . . . . . . . . . . . 49

4.2 Values obtained by direct measurement of the projection (meters) . . . . 50

4.3 Measurement of the distance between the camera and the object (square). 55

4.4 Measurement of circle diameter D=120 mm. . . . . . . . . . . . . . . . . . 56

4.5 Fabric images and inspection process details . . . . . . . . . . . . . . . . . 74

xii

Abbreviations

TP True Positive

FP False Positive

LF Lamination Failure

CF Carving Failure

OP Optimal Point

LG Lamination Growth

SP Stained Piece

mm millimeters

OS Operating System

AUC Area Under Curve

DB DataBase

CE Controlled Environment

GLCM Grey Level Co-occurrence Matrix

CCD Charge Coupled Device

xiii

Chapter 1

INTRODUCTION

1.1 Introduction

The variety of fabric defects, which can change within the same sample, makes it difficult

to automatically detect defects in textiles. The defective raw material that goes unde-

tected along the production represents a waste of time, labor force, storage, packaging,

and transportation. These drawbacks decrease company profit and generate misimpres-

sions on customer or end users. For example, some chairs manufacturing companies have

manual quality inspection systems, which overlook defects in textiles. These issues cause

a negative impact on productivity. Therefore, the goal of this applied research project is

to identify the most costly defects that may occur in fabrics made from polyurethane by

using computer vision and analysis of digital images obtained in controlled environments.

The objects of interest in these images correspond to cloth cuts used to manufacture car

chairs.

The main result of this project will be the design, implementation, and validation of

a computer vision based inspection system to detect defects in cut cloths at INORCA

S.A.S [4]. The remainder of this document is organized as follows. Chapter 1 presents the

definition of the research problem, justification, project objectives, scope and expected

results. Chapter 2 describes the theoretical framework and state of the art in fabric

detection with computer vision. Chapter 3 presents fabric defect detection methods and

feature extraction from fabric images. Chapter 4 presents the experimental results and

analysis. Chapter 5 concludes and describes the future work.

1

Chapter 1. Introduction 2

1.2 Description of the Research Problem

A huge variety of defects can be found on fabrics. This variability makes it difficult

to adopt a general system for quality control on fabrics. Some sources of variability

are lighting, color, density, and compounds. This situation has led to solutions that

are designed to suit every need in specific industries. In the company INORCA S.A.S

the manual inspection of fabric defects is expensive, unreliable and difficult to replicate

throughout the production processes. ¿Therefore, the following question arise: is there

any way to avoid the disadvantages of manual inspection of textiles such as the costs

of labor, lack of reliability and the difficulty of replication? How to detect defects in

fabrics automatically with reliable results and inexpensively?

This project proposes to apply computer vision to inspect the defects that occur on

the fabrics, reduce costs and increase the reliability of inspection results. Compared

to the manual inspection process, computer vision allows to inspect a production pro-

cess without involving human errors caused by fatigue and distractions, facilitating the

quantification of quality variables. It is not to replace the role of a quality inspector,

but to provide him with tools to facilitate his job.

1.3 Justification

Recently, Colombia has signed free trade agreements (TLC for its acronym in Spanish)

with different countries in America and European Union, increasing the potential for

exportation of local manufacturing industries. However, the high cost of raw materials

and the lack of quality control programs increase final product prices leading to loss of

competitivity against companies in other countries where the cost of basic commodities

as steel and fuel are notoriously lower than in Colombia.

Identifying defects in fabric is a major concern for fabric industries, because it influences

brand image and the perception of product quality [5]. The computer vision based

detection of possible defects in fabric is more reliable and consistent than human eye

inspection as demonstrated in previous works [6, 7].

In general, an image of woven fabric sample can be regarded as a typical textured image.

In the textile industry, careful inspections for woven fabrics have to be carried out


because fabric defects may reduce the profit of a company by 45% or 65% [8]. The ways

that an industrial process can be justified depends on the type of applications. However,

in most applications, improvements must be made in the safety and reliability of the

manufacturing process, the product quality, and the development of a new production

process at a minimum cost [9].

In this document we propose to develop a system to automate visual inspection of

textiles in the company INORCA S.A.S. which presents monthly losses of more than

USD 41.000, and where 75% of these losses are due to quality defects in raw materials

made of polyester based fabrics. Common losses are due to:

� Rejection of finished products for defects on fabrics components.

� Unnecessary application of transformations of defective material.

� An application of additional inspections not previously considered in the planning.

� Damage in parts of fabric cut during the production process.

The company pays every month roughly USD 5.000 to eight employees who conduct

visual inspection of fabrics. All these costs decrease competitive power, revenue and

growth of INORCA S.A.S in a globalized market. According to these needs and dis-

advantages, this project is well justified since it will provide an affordable, viable, and

reliable solution to the problem of expensive and unreliable manual inspection of fabrics

in INORCA S.A.S.

1.4 General Objective

Detect defects in composite fabrics of polyurethane using computer vision techniques.

1.5 Specific Objectives

1. Design and implement a controlled environment for image acquisition of cut shapes

of fabric.


2. Analyze, select and implement techniques of segmentation, representation, and

classification of digital images that contains cut shapes of fabric.

3. Design a computer vision based inspection system to detect defects in fabrics at

INORCA S.A.S.

4. Validate the performance of the proposed inspection system based on a comparison

with a manual inspection system.

1.6 Project Scope

The scope of this project focuses on the detection of the most costly defects in five types

of fabrics. The classification of fabrics and detection of defects are based on computer

vision. The intended goal for detection rate is 90%.

It is not part of the scope:

� Statistical based predictions.

� Total automation of inspection system.

� Integration with other information systems.

� Generalization of the model for all fabrics and tissues.

1.7 Expected Project Results

� The current detection rate of human inspection is 85%, according to the goal of

INORCA S.A.S the inspection systems proposed is expected to reach at least 90%

of detection performance.

� Design, implementation, and validation of a computer vision based inspection

system to detect defects in cut cloths at INORCA S.A.S.

Chapter 2

THEORETICAL FRAMEWORK

2.1 Previous works and theoretical background.

One of the most important goals of any production process is to consistently deliver

products of high quality. In order to ensure the product quality some form of quality

control needs to be established. Traditionally, the quality control often involves visual

inspection by human operators. Recently, machine vision is being used to automate this

process. An automatic quality control can offer many advantages, such as an increased

productivity and product quality, and the elimination of human errors. Automating the

visual inspection process requires the knowledge of the human operators to be incorpo-

rated into the software [10].

Computer vision allows to the company performing meaningful visual analysis, avoid-

ing destructive and invasive tests and improving the degree of automation of a quality

monitoring system, and thus enhancing both objectivity and repeatability of the mea-

surement/classification process. Successful applications of artificial vision systems range

from process and manufacturing industry, to medical, pharmaceutical, forensic sciences,

and food engineering [11].

Automatic digital image processing and machine vision systems (MVS) have become

increasingly popular because of the ever-decreasing cost of computing power and the

availability and affordability of digital camera systems. MVS is defined as the use of

devices for optical, non-contact sensing to automatically receive and interpret an image of

a real scene in order to obtain information and/or control machines or processes. MVS

5

Chapter 2. Theoretical Framework 6

is now widely accepted and used within the manufacturing industry for applications

including quality assurance and control [12]. The ways that an industrial process can be

justified depends on the type of applications. However; in general, improvements must

be made in the safety and reliability of the manufacturing process, in product quality,

and enabling technology for a new production process at a minimum cost [9].

There are several works in the field of automatic visual inspection and defect detection in

material surfaces. Surveys of existing techniques are provided by Xie (2008) and Kumar

(2008). Due to the wide fields of application (fabric defect detection, surface analysis,

rail inspection, crack detection, among others.) most of these works focus on a specific

problem domain. Among the statistical approaches, gray level statistics (Iivarinen, 2000;

Chetverikov, 2000; Chetverikov and Hanbury, 2002), co-occurrence matrices (Rautkorpi

and Iivarinen, 2005) and local binary patterns (Niskanen et al., 2001; Maenpaa et al.,

2003; Tajeripour et al., 2008) are the most frequently used ones. Unser and Ade, 1984

and Monadjemi et al., 2004 make use of an Eigenfilter approach. Common spectral

methods are Gabor filters (Mandriota et al 2001; Kumar and Pang, 2002), Fourier

analysis (Chan and Pang, 2000) and wavelet-based approaches (Serdaroglu et al., 2006).

Model-based approaches often model the stochastic variations of a surface with help

of Markov random fields (Cohen et al., 1991). Due to the lack of defective training

samples anomaly detection (Chandola et al., 2009; Markou and Singh, 2003a; Markou

and Singh, 2003b), one-class classification (Tax, 2001) and outlier detection (Hodge and

Austin, 2004) are relevant concepts (Tajeripour et al., 2008; Xie, 2008) [13].

Textile fault detection has been studied using various approaches; one approach (Sari-

Sarraf and Goddard, 1999) uses a segmentation algorithm, which is based on the concept

of wavelet transform, image fusion and the correlation dimension. The essence of this

segmentation algorithm is the localization of defects in the input images that disturb

the homogeneity of texture. Another approach presented in (Daul et al., 1998) consists

of a preprocessing step to normalize the image followed by a second step of associating a

feature to each pixel describing the local regularity of the texture and localize defective

pixels [14].

The development of real-time automated visual fabric inspection systems, in general,

consists of three processes, namely, image acquisition, image processing, and image


analysis. Typical examples include Elbit Vision Systems I-TEX system [15], BarcoVi-

sion’s Cyclops[16] and Zellweger Uster’s Fabriscan [17]. These systems inspect fabric in

full width at the output of a finishing machine. They are designed to find and catalog

defects in a wide variety of fabrics including greige fabrics, sheeting, apparel fabrics,

upholstery fabrics, industrial fabrics, tire cord, finished fabrics, piece-dyed fabrics and

denim. However, they cannot inspect fabrics with very large and complex patterns.

Other examples include a fuzzy wavelet analysis system for process control in the weav-

ing process, a vision system for on loom fabric inspection, a vision system for on-circular

knitting machine and a vision system for fabric detection based on different Gabor fil-

ters. These systems are rather expensive which prevent them from being widely adopted

by small to medium sized factories [18].

Texture analysis has various applications in different areas on computer vision, image

processing, medical image processing and related fields. There is no clear-cut definition

for image texture. Image texture is believed to be a rich source of visual information –

about the nature and 3D shape of physical objects (Materka and Strzelecki, 1998). Tex-

tures are complex visual patterns composed of entities, or sub-patterns – that have char-

acteristic brightness, color, slope, size, among others. Hence, texture can be regarded

as a similarity grouping in an image (Rosenfeld and Kak, 1982). The local sub-pattern

properties give rise to the perceived lightness, uniformity, density, roughness, regularity,

linearity, frequency, phase, directionality, coarseness, randomness, fineness, smoothness,

granulation, among others, of the texture as a whole (Materka and Strzelecki, 1998).

In another definition, the texture of images refers to the appearance, structure and ar-

rangement of the parts of an object within the image (Castellano et al., 2004). Smarter

extraction of features from image textures produce better cues for image analysis, which

are pivotal for object recognition, surface analysis, action recognition, disease diagno-

sis, among others. There are various approaches for texture analysis. Most of these

approaches are based on structural, model-based, statistical (e.g., histogram, absolute

gradient, run-length matrix, co-occurrence matrix, auto-regressive model, wavelets) [19].

In [20] Mahajan, Kolhe and Patil propose an important starting point to classify the

approaches used so far in the representation of defects on fabrics. These approaches are

statistical, spectral, and model based which are described below.


2.1.1 Statistical Approaches

Statistical texture analysis methods measure the spatial distribution of pixel values.

An important assumption in this approach is that the statistics of defects free regions

are stationary, and these regions extend over a significant portion of inspected images.

The first-order statistics estimate properties like the average and variance of individual

pixel values, ignoring the spatial interaction between image pixels. Second and higher

order statistics estimate properties of two or more pixel values occurring at specific

locations relative to each other. The defect detection methods employing texture features

extracted from fractal dimensions, first order statistics, cross correlation, edge detection,

morphological operations, co-occurrence matrix, eigenfilters, rank order functions, and

many local linear transforms have been categorized into this class [20].

2.1.2 Spectral Approaches

In spectral based approaches texture is characterized by texture primitives or texture

elements, and the spatial arrangement of these primitives. Thus, the primary goals

of these approaches are firstly to extract texture primitives, and secondly to model or

generalize the spatial placement rules. The high degree of periodicity of basic texture

primitives, such as yarns in the case of textile fabric, allows the usage of spectral features

for the detection of defects. However, random textured images cannot be described in

terms of primitives and displacement rules as the distribution of gray levels in such

images is rather stochastic. Therefore, spectral approaches are not suitable for the

detection of defects in random texture materials. Various approaches for the detection

of defects in uniform textured material using frequency and spatial-frequency domain

features have been reported in the literature. In spectral-domain approaches, the texture

features are generally derived from the Fourier transform, Gabor transform and Wavelet

transform [20].

2.1.3 Model-Based Approaches

Model - based texture analysis methods are based on the construction of an image model

that can be used not only to describe texture, but also to synthesize it. Model-based

approaches are particularly suitable for fabric images with stochastic surface variations


(possibly due to fiber heap or noise) or for randomly textured fabrics for which the sta-

tistical and spectral approaches have not yet shown their utility. The model parameters

capture the essential perceived qualities of texture. Markov random fields (MRF) have

been popular for modeling images. MRF theory provides a convenient and consistent way

for modeling context dependent entities such as pixels, through characterizing mutual

influences among such entities using condition MRF distribution. Several probabilistic

models of the textures have been proposed and used for defect detection; Cohen (1991)

used Gaussian Markov Random Fields (GRMF) to model defect free textile web. The

inspection process was treated as a hypothesis testing problem on the statistics derived

from the GMRF model. The images of fabric to be inspected are divided into small win-

dows in inspection process. A likelihood ratio test is then used to classify the windows

as non– defective or defective. The testing image is partitioned into non-overlapping

sub-blocks where each window was then classified as defective or non-defective [20].

Previous works show two fabric defect detection approaches. One is based on transform

domain like Gabor filters and another hinges on statistical texture analysis like Gray

Level Co-occurrence Matrix (GLCM). However few other versions of these models and

different models are also available. Karayiannis (1999) presented multi-resolution de-

composition based real time fabric defect detection system (FDDS). Cohen (1991) has

characterized the fabric texture using the Gauss Markov random field (GMRF) model

and the fabric inspection process is treated as a hypothesis-testing problem on the statis-

tics derived from this model and classifying textile faults with a back-propagation neural

network using power spectrum. Zuo (2012) used NL-means filtering algorithm for tex-

ture enhancement where GLCM was used with Euclidean distance to find defects. He

showed overall detection rate of 88.79%. Siew has shown assessment of carpet wear using

spatial gray level dependence matrix (SGLDM) [8].

The most commonly used features are the second-order statistics derived from spatial

gray-level co-occurrence matrices. The fabric texture exhibits a high degree of period-

icity, and thus Fourier-based methods characterize the spatial-frequency distribution of

textured images, but they do not consider the information in the spatial domain and

may overlook local deviations [8].

Defect detection, which interests quality inspection, separates products into two main


classes, acceptable if there is no defect in product and not acceptable if there is any de-

fect. In contrast, defect classification determines which kind of defect has been occurred.

It could help maintenance personnel to spot the type of needed repairing of product line.

Defect detection could be considered as a specific case of defect classification and two

tasks are basically similar in most practical steps including image acquisition, prepro-

cessing, feature extraction and arithmetic operations for inferring and decision making

[21].

Table 2.1: Fabric defect detection methods.

Approach Method References

Statistical

1. Co-occurrence matrix [5, 20, 22–24]2. Combined Algorithms [20, 25–27]3. Regular Band [28]4. Adaptive Dictionaries [29]5. Local Linear Transforms [20, 23]6. Fractal Dimension [20, 23, 30]7. Edge Detection [20, 23]8. Cross-correlation [20, 23]9. Bi-level thresholding [20, 23]10. Morphological Operations [18, 20, 23]11. Histogram [20, 22, 23]

Spectral1. Discrete Fourier Transform [12, 19, 23, 31]2. Gabor Filter [5, 8, 20, 23, 26, 31–34]3. Wavelet Transform [20, 22, 23, 35–37]

Model Based1. Gauss Markov Random Field [20, 23]2. Poissonian model [20, 23]3. Model-based Clustering [20, 23]

Despite the large number of methods, to determine the best technique is a topic that

has to do directly with the specific problem, Table 2.1 summarizes the state of the art

for the approaches mentioned above

2.2 Camera Calibration

Defect detection in fabrics requires the relationship between camera measurements with

dimensions in the 3D real world. This relationship quantifies sample dimensions based

on pixels obtained in the camera. This section, describes camera calibration to estimate

intrinsic and extrinsic parameters in a controlled environment for computer-based fabric

defect detection. The computer vision set of libraries OpenCV (Open Source Computer

Vision Library) [38] calibrates Flea3 20S4C-C Color GigE [39] Camera in a controlled


environment. This section also presents the design, implementation and testing of the

controlled environment for image acquisition.

Camera calibration is a necessary step in automatic inspection of fabric defects to ex-

tract metric information from 2D images. It provides a model of camera geometry and

distortion model of the lenses, Tables 2.2 and 2.3 summarize the components involved

in the calibration, the specific reason for using this camera is the speed of transmission

with GigE connection and representation capacity obtained with the combination of

lens/sensor. Figures 2.1 and 2.2 show the camera and the lens deployed in this work.

The calibration process is based on pinhole camera model in which, a single ray of light

enters the camera from the scene or a distant object to be projected on an imaging

surface.

Table 2.2: Hardware

Lens 6 mm Compact fixed focal length

Connection Cat 5e GigE cable, RJ45 type

Power Supply Universal Power Supply and GPIO Leads

Table 2.3: Camera specifications

Model Number FL3-GE-20S4C-C

Camera Sensor Format 1/1.8”

Imaging Device Progressive Scan CCD

Type of Sensor Sony ICX274

Pixels (H x V) 1624 x 1224

Pixel Size, H x V (µ m) 4.4 x 4.4

Frame Rate (fps) 15

Figure 2.1: Point Grey - Flea3 FL3-GE-20S4C-C Camera.

In consequence, the size of the image projected on the projective plane is controlled by

camera’s focal length. For our idealized pinhole camera, the distance from the pinhole

aperture to the screen is precisely the focal length. Figure 2.3 presents this model, where


Figure 2.2: Edmunds Optics - 6 mm Compact Fixed Focal Length Lens.

f is the focal length of the camera, Z is the distance from the camera to the object, X is

the length of the object, and x is the object’s image on the imaging plane.

Figure 2.3: Pinhole model with identification of key variables

2.2.1 Image Formation

The relation that maps the point Pi into the physical world with coordinates q =

(xi, yi, zi) to the point on the projection screen with coordinates u, v is called projective

transform.

Parameters that define the camera such as (fx, fy, cx, and cy) are intrinsic. Where fx

and fy are focal lengths in x and y directions. cx and cy represent a possible displace-

ment (away from the optic axis) of the center of coordinates on the projection screen.

Extrinsic parameters relate the object’s position relative to the camera coordinate sys-

tem in terms of rotation and translation matrices.


2.2.2 Homogeneous coordinates

The homogeneous coordinates of a point in three dimensions represented in Cartesian

coordinates (X,Y, Z)T are defined as the point (kX, kY, kZ, k)T where k is an arbitrary

constant different to 0. A point P of the real-world space represented by Cartesian

coordinates can be expressed in a vector form as [1]:

P =

X

Y

Z

In homogeneous coordinates:

Ph =

kX

kY

kZ

k

2.2.3 Perspective projection

As indicated above the projection perspective explains the formation of images in a

camera whose functional model is represented by the pinhole model. Figure 2.4 shows

the formation of an image using the perspective projection. Rw is the coordinate system

of the world where object of interest is located, Rc is the coordinate system centered at

the optical center of the camera, and Ri is image coordinates.

Points in the real world are represented on a three dimensional basis, while points

on the image are two dimensional. The relation that maps points Qi in the physical

world represented with coordinates (Xi, Yi, Zi) to the points on the projection screen

with coordinates (xi, yi) is called a projective transform. The homogeneous coordinates

associated with a point Q in the projective space of dimension n are typically expressed

as an (n+1) dimensional vector (e.g., x, y, z becomes x, y, z, w). Clearly the optical axis

is aligned with the Z axis of the reference system of the camera. The center of the image

plane coincides with the origin of both systems, in which Z axis loses importance for

model reference since it becomes a constant value.


(x) (X)

( , , )X Y Z

{ }Rw

{ c}R

(y) ( )Y

Image plane

(x,y)

focal distance

(z) (Z)

Figure 2.4: Modeling perspective projection [1]

In this work, the image plane corresponds to the projective space and it has only

two dimensions, so we will represent points on that plane as three-dimensional vec-

tors q = (q1, q2, q3). All points having proportional values in the projective space are

equivalent, division by q3 recovers the actual pixel coordinates. This allows us to arrange

the parameters that define our camera (i.e., fx, fy, cx, and cy) into a single 3-by-3 ma-

trix, which we will call the camera intrinsics matrix parameters (the approach used by

OpenCV compute to camera intrinsics is derived from Heikkila and Silven [Heikkila97]).

Let’s supposse a point in space with coordinates Mc = (Xc, Yc, Zc)T , and its correspond-

ing image coordinates mi = (xi, yi), Zc > f , indicates that the points of interest are

located in front of the camera. Figure 2.5 shows the relationship between world and

image coordinates by triangle similarity.

Figure 2.5: Relationship between world and image coordinates.


xif

= − Xc

Zc − f=

Xc

f − Zc⇒ xi =

fXc

f − Zc(2.1)

yif

= − YcZc − f

=Yc

f − Zc⇒ yi =

fYcf − Zc

(2.2)

Then

m =

x

y

=

fXc

f−Zc

fYc

f−Zc

(2.3)

The first two components are the coordinates m(x, y) in the image plane of the three-

dimensional point (X,Y, Z) projected. As mentioned above, the third component, z, is

constant in this plane. In the Figure 2.3 the negative sign in front of Xc and Yc indicates

that the image is inverted. These equations are nonlinear because they contain a division

by Zc. For geometric transformations it is suitable to express these expressions in matrix

form by converting them into homogeneous coordinates. The homogeneous coordinates

of the point are Mch = (KXc,KYc,KZc,K)T where K is an arbitrary nonzero constant.

Mih is an image point in homogeneous coordinates. Defining B as a perspective matrix

that relates the points of space with the image points in homogeneous coordinates.

B =

1 0 0 0

0 1 0 0

0 0 −1f 1

BMch = mh

1 0 0 0

0 1 0 0

0 0 −1f 1

∗

kXc

kYc

kZc

k

=

kXc

kYc

k(

f−Zc

f

)


2.2.4 Homogeneous transformation

So far the coordinate system of the camera coincides with the world, i.e the experimental

platform where the fabric to be inspected. A more general case involves a transforma-

tion that converts the world coordinates Rw into the camera Rc coordinates as seen in

Figure 2.6.

Z

{ }Rw

Y

X

{ }Rc

Camera

x

y

z

Figure 2.6: Final position (translation and rotation)

To obtain the complete pinhole camera model it is necessary to scale and translate the

image. Scaling occurs in a rectangular grid with dy scale factors in the vertical direction

and dx in the horizontal direction, the angle between the x axis and the y axis is defined

by the skew coefficient, which is only equal to zero if the angle is positive.

The following steps must be followed to transform the camera coordinate system into

world coordinate system:

1. Rotation around the x axis with angle α

2. Rotation around the y axis by an angle β

3. Rotation around the z axis with angle θ

The translation is represented by (x0, y0). The values x∗i and y∗i are the coordinates of

the point after applying the central projection, being xi, yi the coordinates in the system

image, they are listed according to the following equations:

{xi = x0 + dxx∗i } (2.4)


{yi = y0 + dyy∗i } (2.5)

From the above we have:

xi

yi

1

=

dx 0 x0

0 dy y0

0 0 1

1 0 0 0

0 1 0 0

0 0 −1f 1

Xc

Yc

Zc

1

(2.6)

Multiplying (2.6) by focal length f we obtain (2.7)

f

xi

yi

1

=

dxf 0 x0 0

0 dyf y0 0

0 0 1 0

Xc

Yc

Zc

1

(2.7)

Equations (2.8), (2.9) and (2.10) corresponds to rotations matrices.

Rα =

1 0 0 0

0 cosα sinα 0

0 − sinα cosα 0

0 0 0 1

(2.8)

Rβ =

cosβ 0 − sinβ 0

0 0 1 0

sinβ 0 cosβ 0

0 0 0 1

(2.9)

Rθ =

cos θ sin θ 0 0

− sin θ cos θ 0 0

0 0 1 0

0 0 0 1

(2.10)


Since the translation matrix must be concatenated with other rotation matrices it is

convenient to convert it into a square matrix using (2.11).

Tr =

1 0 0 rx

0 1 0 ry

0 0 1 rz

(2.11)

Where rx, ry and rz are the translation components in x, y, z, and Tr represents the

translation matrix. Rα , Rβ and Rθ are rotation matrices around angles α, β and θ,

respectively.

x∗

y∗

z∗

=

1 0 0 rx

0 1 0 ry

0 0 1 rz

x

y

z

1

→

x∗ = x+ rx

y∗ = y + ry

z∗ = z + rz

(2.12)

Final integration of rotation matrix is:

R =

cosβ cos θ cosβ sinα − sinβ 0

sinα sinβ − cosα sinβ sinα sinβ sin θ + cosα cosβ sinα cosβ 0

cosα sinβ cos θ − sinα sinβ cosα sinβ cos θ − sinα sinβ cosα cosβ 0

0 0 0 1

(2.13)

The composition of the translation and rotation results in matrix E shown in (2.14).

E =

R Tr

0T 1

(2.14)

Appendix B presents the experimental configuration. Camera’s optic axis is parallel to

surface’s normal vector on which the fabric sample will be placed.

2.2.5 Camera parameters

2.2.5.1 Intrinsic parameters

Camera intrinsic parameters depends on factors such as focal length, CCD (Charge-

Coupled Device) dimension, and lens distortion. Heikkila[97] defines the effective focal


length f, scale factor s, and the image center (u0, v0) also called the principal point as

intrinsic parameters. Equation (2.7) permits to find these parameters as:

fx = dxf (2.15)

fy = dyf (2.16)

cx = x0 (2.17)

cy = y0 (2.18)

xscreen = fx

(

X

Z

)

+ cx (2.19)

yscreen = fy

(

Y

Z

)

+ cy (2.20)

The focal lengths fx and fy are different since the individual pixels on a typical low-cost

imager are rectangular rather than square. The focal length fx is actually the product

of the physical focal length of the lens and the size sx of the individual imager elements.

Generally the center of the sensor is not in the optical axis. Therefore parameters, cx

and cy are used to model possible shift (distance from the optical axis) of the center

coordinates in the projection screen. The result is a relatively simple model in which a

point Q in the physical world, with coordinates (X,Y, Z) is projected on the screen at

any given pixel location (Xscreen, Yscreen).

2.2.5.2 Extrinsic parameters

As opposed to the intrinsic parameters that describe internal parameters of the camera

(focal distance, radial lens parameters), the extrinsic parameters indicate the exter-

nal position and orientation of the camera in the 3D world. Camera extrinsic matrix

describes the transformation of world coordinates into camera coordinates, Figure 2.7

shows the information flow from real-world coordinates to camera coordinates (i.e from

3D to 2D).

Extrinsic parameters consists of three components:

1. Translational component Tx, Ty, Tz; seen before in equation 2.11


2. Distance component (camera optical center).

3. Rotation component Rα, Rβ, Rθ seen in equations 2.8, 2.9 and 2.10 respectively.

Object Coordinates 3D

World Coordinates (3D)

Camera Coordinates 3D

Image Plane Coordinates (2D)

Pixel Coordinates (2D)

}}

extrinsic cameraparameters

intrinsic cameraparameters

Figure 2.7: Order of applying parameters from the real world to the camera

2.2.6 Lens distortions

The estimation of object’s size is an important task in optical measurement. Lens distor-

tion affects its performance. This study characterizes radial and tangential distortions

in the lenses that conform the computer vision based measurement system. Figure 2.8

shows a type of distortion known as the barrel distortion by deformations in the edges

of the image, a defect is clearly associated with the lens.

lens

Image PlaneO jectb

Radial Distortion

Figure 2.8: Example of radial distortion (barrel distortion). [2]

2.2.6.1 Radial distortion

The lenses of real cameras often noticeably distort the location of pixels near the edges

of the imager. This phenomenon is the origin of the barrel or fish eye effect. Figure 2.8

illustrates the reasons of radial distortion. With some lenses, rays farther from the center


of the lens are bent more than those closer in. The parameters associated with the radial

distortion are named as k1 and k2.

For radial distortion, the distortion is 0 at the (optical) center of the imager and in-

creases as we move toward the periphery. In practice, this distortion is small and can

be characterized by the first few terms of a Taylor series expansion around r = 0. For

low-cost web cameras, we generally use the first two terms; the first called k1 and the

second is k2. In general, the radial location of a point on the imager will be rescaled

according to the following equations:

xrcorrected = x(1 + k1r2 + k2r

4 + k3r6) (2.21)

yrcorrected = y(1 + k1r2 + k2r

4 + k3r6) (2.22)

Here (x, y) is the original location (on the imager) of the distorted point and (xrcorrected, yrcorrected)

is the new location as a result of the correction.

2.2.6.2 Tangential distortion

The second-largest common distortion is tangential distortion. This distortion is due to

manufacturing defects resulting from the lens not being exactly parallel to the imaging

plane. Tangential distortion is minimally characterized by two additional parameters p1

and p2, such that:

xtcorrected = x+ [2p1y + p2(r2 + 2x2)] (2.23)

ytcorrected = y + [p1(r2 + 2y2) + 2p2x] (2.24)

Thus in total there are five distortion coefficients that we require. Because all five are

necessary in most of the OpenCV routines that use them, they are typically bundled

into one distortion vector; this is a 5× 1 matrix containing k1, k2, p1, p2, and k3.

2.3 Illumination systems

Illumination plays an important role in the development of computer vision based in-

spections, whether automated or manual. The presence of shadows negatively impacts


the performance of segmentation and classification algorithms. Table 2.4 describes the

amount of light recommended to carry out different activities that require the visual

attention. The average luminance is given by:

Lum =LuminousF lux× Utilizationfact×Maintenancefact

(area)m2 (2.25)

Table 2.4: Preferred sustained luminance levels for different locations or visual tasks- Encyclopedia of health and safety at work [3].

Task/Location Recommended LuxOffice 500Computerized workstations 500Manufactures mounting areas 500Low precision work 300Medium accuracy work 500Precision work 750Assembling Instruments 1000Installation and repair of jewelry 1500Operating rooms 50000

The criteria taken into account to define the type of illumination are:

1. Luminance provided by the source must be equal or greater than the illumination

required by a human to execute high precision task that requires visual attention.

2. Durability of at least one year.

3. Adaptivity to several heights and orientations with respect to the inspected surface.

4. Operation voltage ranging from 110V to 220V.

It’s difficult to find information about lighting configurations for fabrics inspection. How-

ever, by taking into account the requirements described above, an array of tubular lamps

with LED technology was selected. Table 2.5 shows technical facts of this bulb provided

by the manufacturer [40].

Each tubular bulb has a length of 1.20 m (as shown in Figure 2.9). Based in pinhole

model calculate the lighting coverage in the scene that contains the fabric sample to be

inspected, the illumination set-up is made of two bulbs, at a distance (h) of 1.18 m from

the table to the lamps. This configuration provides an illuminated region on the table


Table 2.5: Information provided by the manufacturer of T8 LED Tube 22W.

Item ValueNominal power 22WNominal voltage 110-220V.Nominal Electrical Current 290 mA.Lumen flux 2250 lumensMaintenance factor 0.65Standard IEC 60968Projection Angle 80◦

of sizes 1.678 m and 0.7031 m, with a luminance of 1463 lux.

tan(80/2) =Area

1

Area = tan(40)× 1 m× 2

Area = 1, 678 m2

Figure 2.9: Tubular bulb, T8 led 22W - 1.20 m.

The above calculation provides an important hint to determine the appropriate distances

between the fabric and the illumination source. This means that to attain an illuminated

area with 1.678 m2 wide the lamps must be placed at a minimum height (h) of 1.18 m.

That is:

tan(80/2) = (h/2)/xMax

tan(80/2) = (1.18 m/2)/xMax

xMax = 0.7031 m.

Where xMax is the height of the effective surface. The illumination set-up contains two

tubes spaced by 10 cm.

2.4 Fabrics and selection of defects

Segmentation of a given defect with prior knowledge of its shape, size or distribution is

known as supervised defect segmentation. For the scope of the present work, we selected

five types of fabrics with defects. Table 2.6 shows the main defects presented in fabrics at

INORCA company, between January and September 2013, this analysis includes 97.9%


of all defects. The remaining 2.91% corresponds to defects that occur with an individual

percentage less than 0.01%, thus this ranking does not consider them.


2.4.1 Main defects ranking

Table 2.6: Ranking of the main defects on fabrics occurred between January andSeptember 2013 detected by human inspection

Fabric defect cost in US DollarsFabric Cost by defect

# Continuity Scratch No-cont LF CF SF DT. Total %tissue

1 Kariatyde 495 3.509 9.237 0 4.289 0 0 17.530 22.49

2 Vulcron vinyl W/L 0 9.084 0 0 5.845 0 137 15.066 19.33

3 Vulcron vinyl N/L 0 6.154 0 0 5.284 0 0 11.438 14.67

4 Petit point gray 0 4.878 0 0 4.734 0 0 9.611 12.33

5 Petit point red 0 5.740 420 159 0 0 0 6.319 8.11

6 Baltic vinyl 0 0 0 0 2.545 0 176 2.722 3.49

7 Geneva 0 1.672 38 46 0 0 836 2.592 3.32

8 Craquele 268 1.443 0 0 60 0 0 1.771 2.27

9 Pliss 0 1.181 0 0 0 0 409 1.590 2.04

10 Roseau 77 1.164 0 328 0 0 0 1.568 2.01

11 Kario carbone 0 443 0 0 703 0 0 1.145 1.47

12 Baji 0 1027 0 0 0 0 0 1.027 1.32

13 Kario black normal 0 76 412 0 445 0 0 933 1.20

14 Perle copo 0 495 189 0 17 0 0 701 0.90

15 Kario orange 0 309 0 0 358 0 0 667 0.86

16 Corsario Billiard 0 0 0 0 185 296 0 481 0.62

17 Poesy mod copo 0 416 16 0 0 0 0 432 0.55

18 Kario black LG 2 mm 0 143 32 0 127 0 0 302 0.39

19 Duo carbon fonce 0 180 0 0 69 0 0 249 0.32

21 Demo blue 0 99 21 0 0 0 0 120 0.15

22 No-woven unspecific 0 0 0 0 25 25 0 50 0.06

23 Embossed GT Line 1 0 3 0 1 0 0 5 0.006

Total 840 39.353 10.369 533 24.984 320 1.159 77.958 97.90

2.4.2 Defects criteria selection

Based on the analysis of financial costs generated by defects in different types of fabrics,

a total of five fabrics types are selected to be detected as listed in Table 2.7. From these

fabrics two defects were analyzed; a defect for each type of fabric. However it is possible

that on the same fabrics other defects may occur. Next part describes the appearance

of selected defects.

Continuity : corresponds to problems caused by incorrect splicing fabrics

Scratch: corresponds to the marks made by operators on fabrics indicating that in these

places there may be problems, operators mark with pen if the fabric is a white synthetic

surface or with white chalk in case it is a woven fabric. Commonly, these strokes may

be difficult to identify by operators due to long work operation hours. Figures 2.11e and

2.11c illustrates the default scratch in a synthetic fabric and a woven fabric respectively.


Table 2.7: Main defects selected under the criterion of the highest cost

Fabric defect cost in US DollarsFabric Cost by defect

# Fabric Continuity Scratch Noncontinuous LF CF SF DT Total %tissue

1 Kariatyde 9.237 9.237 11.85

2 Vulcron vinyl W/L 9.084 9.084 11.65

3 Vulcron vinyl N/L 6.154 6.154 7.89

4 Petit point gray 4.878 4.878 6.26

5 Petit point red 5.740 5.740 7.36

Total 0 25.856 9.237 45.02

Noncontinuous tissue: is the lack of a thread in the sequence of tissue and usually

appears as a dotted straight line as seen in Figure 2.11a, this type of defect usually has

a width of 0.2 and 0.5 mm.

LF : lamination failure: are errors in the laminate process of fabrics.

CP : crumpled piece: are caused by failures in the treatment of fabrics.

SF : stained fabric: are caused by failures in the treatment of fabrics.

DH : differences in tones: are caused by faults in the manufacturing process of the fabrics.


(a) Karyatide. (b) Gray petit.

(c) Red petit.

(d) Vulcron vinyl WL. (e) Vulcron vinyl NL.

Figure 2.10: Selected fabric images


(a) Karyatide - noncontinuous tissue. (b) Gray petit - scratch.

(c) Red petit - scratch.

(d) Vulcron vinyl WL - scratch.1

1 defect located on the left side(vertical).

(e) Vulcron vinyl NL - scratch2

2 defect located on the center(diagonal).

Figure 2.11: Selected images of defective fabrics.

Chapter 3

DEFECT DETECTION AND

FABRICS REPRESENTATION

3.1 Defect detection in fabrics

After calibrating the camera, and selecting the types of fabrics and defects to be clas-

sified, it’s very important to determine a strategy to segment the defects in an image.

In this work, we select Gabor filters as a representation for defect segmentation in three

fabrics types (kariatyde, petit-point gray and petit-point red). Simple thresholding seg-

ments two fabrics: Vulcron Vinyl WL and Vulcron Vinyl NL. The surfaces of these

fabrics do not present a specific orientation; hence Gabor filtering is not a suitable for

their representation.

3.1.1 Gabor filters

Multichannel Gabor filters are a joint spatial/spatial-frequency representation for an-

alyzing textured images with highly specific frequency and orientation characteristics.

This technique extracts features by filtering the textured image with a set of Gabor filter

banks characterized by the frequency, the orientation of the sinusoid, and the scale of

the window function [27].

29

Chapter 3. Defect detection and fabrics representation 30

The transform of Gabor was initially defined by Gabor in [41] and extended by Daugman

in [42]. The Gabor filter is in practice a linear filter whose impulse response is a sinusoidal

function multiplied by a Gaussian function.

The application of the of Gabor filters is provided by a bank of filters with size M ×N ,

where M is the number of dilations and N is the number of rotations, G Daugman

(2002) found that the response of simple cells in the visual cortex of the mammalian

brain can be modeled by Gabor functions. Therefore, the image analysis by Gabor

functions is similar to the human visual perception system. The Gabor filter bank has

been extensively studied in visual inspection. Kumar and Pang (2000) perform fabric

defect detection using only real Gabor functions. Later in 2002, they used a class of self

similar Gabor functions to classify fabric defects [20].

Gabor filters decomposes images into different scales and orientations hence they may

highlight defects in fabrics. Gabor filter for fabric defect detection has good perfor-

mances both in spatial domain and in frequency domain. In general, an even symmetric

Gabor filter is good at detecting blob-shaped fabric defects, while an odd symmetric one

performs well in detecting edge-shaped fabric defects. In this method, even symmetric

and odd symmetric Gabor filter masks are used for better defect detection [8].

As mentioned earlier, in the spatial domain, the Gabor function is a complex exponen-

tial modulated by a Gaussian function. The Gabor function forms a complete but a

nonorthogonal basis set and its impulse response in the two–dimensional (2-D) plane

has the following general form [41]:

f(x, y) =1

2πσxσy

[

− 12

(

x2

σ2x

+ y2

σ2y

)](2πju0x)

(3.1)

Where u0 denotes the radial frequency of the gabor function, σx and σy define the

Gaussian envelope along the x and y axis. Figure 3.1 shows the perspective plot of a

typical Gabor filter in the spatial domain.

In the frequency domain, the Gabor function acts as a bandpass filter and the Fourier

transform of f(x, y)is given by:

F (u, v) = exp

{

−1

2

[

u− u20σ2u

+v2

σ2v

]}

(3.2)


Figure 3.1: Components of a Gabor function in spatial domain.

where

σu =1

2πσxand σv =

1

2πσy

Using (3.1) as the mother Gabor wavelet, the self-similar filter bank can be obtained by

appropriate dilation and rotation of the generating function:

fp,q = α−P f(x′, y′) (3.3)

where

x′ = α−P (x cosθq + y sin θq)

= α−P (−x sin θq + y cos θq)

α > 1 p = 1, 2, ..., S;q = 1, 2, ..., L.


The integer subscripts p and q represent the index for scale (dilation) and orientation

(rotation), respectively. S is the total number of scales and L is the total number of

orientations in the self-similar Gabor filter bank. For each orientation q the angle θq is

given by:

θq =π(q − 1)

L, q = 1, 2, ..., L. (3.4)

The scaling factor α−P ensures that the all energy image Epq =∫ −∞

∞

∫ −∞

∞|fpq(x, y)|2 dxdy

is independent of p [43]. Thus, all the filters in the Gabor filter bank have the same

energy, irrespective of their scale and orientation.

This work is centered on asymmetric Gabor filters, this mean σx 6= σy; asymmetric

Gabor filters can be useful for real fabric textures, Gabor function does not exactly

satisfy the requirements that the wavelet be admissible and progressive [43]. However,

in the context of representing a class of self-similar functions, this term is used. The

following formulas ensure that the half-peak magnitude responses of adjacent asymmetric

filters touch each other [34]:

α =

(

θhθ

)

α−P =

(

θhθ

)−( 1(s−1)

)

σx =

√2 ln 2(α+ 1)

2πσxθh(α− 1)

thus,

σy =

[

2 ln 2−(

2 ln 2

2πσxθh

)2]1/2

[

2π tan( π

2L

)

(

θh − 2 ln 2

(

1

4π2σ2xθh

))]−1(3.5)

where u0 = θh, while θl and θh are the lowest and highest frequencies. A bank of self-

similar Gabor filters formed by rotation (varying q) and dilation (varying p) is used to


perform power spectrum sampling of the inspection images.

Each of the Gabor filters value has the real part (even) and imaginary part (odd), that

are conveniently implemented as the spatial mask of M ×M sizes. In order to have a

symmetric region of support M is preferred to be an odd number. For a given input

image I(x, y) the magnitude of filtered image Ipq(x, y) is obtained by using Gabor filter

fpq(x, y) as follows:

Ipq(x, y) ={

[fpq(x, y)e ∗ I(x, y)]2 + [fpq(x, y)o ∗ I(x, y)]2}1/2

(3.6)

where ”*” denotes 2-D convolution operation, and fpq(x, y)e and fpq(x, y)o represent the

even and odd parts of the Gabor filter separated from (3.4)

Gabor filter analysis provides an image for each pair of orientation and scale values.

Gabor filters outputs reveal the orientations and scales of defects in the fabrics with

defined textured patterns. Although there is a limited amount of a priori information

about the defects such as shape and texture, it is necessary to tune the filter bank

parameters to find those that provide the highest defect detectability.

3.1.1.1 Gabor filter selection method

A bank of Gabor filters of S scales (p) by L orientations (q) is applied on a real fabric

image sample with a defect free of noise. This image is divided into non-overlapping

square regions K of size l× l pixels, to avoid unnecessary reprocessing of regions, Gabor

filter banks are convolved with each region k obtaining an output image Ipq. In this

processed image, each region is represented with the average value in equation (3.7) .

Dik =

1

(lxl)

∑

(x,y)∈k

Ipq(x, y) (3.7)

The next step takes the maximum and minimum average values to set the cost function

Ji for each filter in the bank [44]:

J(i) =

(

Dimax −Di

min

Dimin

)

(3.8)


The filter f(x, y)rp that gives the highest cost function is chosen as the best representative

filter to detect the class of fabric defects under consideration.

J(rp) = maxl≤i≤SxL

{J(i)} (3.9)

The image under inspection is filtered with f(x, y)rp, which provides the highest cost

function Jrp in 3.9. The magnitude of this filtered image is obtained using 3.6. Thresh-

olding operation segments the defects in this image.

3.1.1.2 Defect detection

The convolution of Gabor filter banks with fabric image provides the inputs to the

segmentation stage of fabrics karyatide, Vinyl Vulcron WL and Vinyl Vulcron NL. This

segmentation stage is based on thresholding the resulting image with a value obtained

from a reference defect-free fabric image. Gabor filters banks f(x, y)ga convolve this

reference image obtaining a magnitude image R(x, y). The maximum of R(x, y) becomes

the threshold Ψh:

Ψh = maxx,y∈W

|R(x, y)| (3.10)

The minimum of R(x, y) becomes the threshold:

Ψl = minx,y∈W

|R(x, y)| (3.11)

Where W is a window centered at the image. The range of W is [Ψh,Ψl].

3.1.1.3 Gabor filter parameters

In this work Gabor bank of filters parameters are:

• Kernel size: defines mask size with which the filtering is performed.


• Kernel angle steps (θ/n): define the number of angular scales.

• Kernel frequency steps (f(θ/n)): define the number of frequency scales.

• Low frequency (θl): defines the smallest frequency in radians.

• High frequency (θh): defines the largest frequency.

A genetic algorithm will estimate the optimal limits (θl) and (θh) to maximize the true

positive rate in the defect detection, an illustration of the search process parameters by

genetic algorithms can be seen in Figure 4.24, section 4.3.1 describes and discusses the

process.

Figure 3.2 shows the Gabor filter parameters for inspection process.

θ

θ/nθlθh

f( /n)θ

Figure 3.2: Gabor filter parameters

Appendix A shows the user interface options to set these parameters.

3.2 Feature extraction for fabrics representation

In [45] and [46], authors identify both defect and fabric based on the features as en-

ergy images (Wavelet decomposition), in [8] Raheja works with features as maximum


probability, energy, entropy, dissimilarity and contrast among others for texture charac-

terization.

Previous works have represented fabrics based on features extracted from the image such

as histograms [45] and geometric details [47]. Nonetheless, this work deploys statisti-

cal features such as mean, variance, kurtosis, and skewness to represent fabric images

convolved with Gabor filter banks.

In this work the features that represent fabric and defect are different, we propose

fabrics representation independently of the process of identifying defects. This selection

considers the texture of the fabric as a differentiator, specifically there are two fabrics

with the same tissue type and different color.

• Mean is the average value of the image:

µ =

N∑

i=1

xi. (3.12)

• Variance is the relative dispersion of pixels intensities with respect to their mean.

σ2 = (1/(N − 1))∑

(xi − µ)2. (3.13)

• Skewness is the third central moment of pixel intensities.

µ3 = E

[(

X − µ3

σ

)]

(3.14)

Then,

sk =µ3

σ3(3.15)

where µ3 stands for the third moment about the mean, and σ is the standard

deviation.

• Kurtosis is the fourth central moment [48] of pixel intensities.

µ4 = E[

(X − µ)4]

(3.16)


Then,

ku =µ4

σ4(3.17)

These values are extracted from images resulting from convolving Gabor filters with

images of fabrics. Figure 3.3 shows the process to obtain the statistical features (mean,

variance, kurtosis and skewness) for a training set.

Selectimage folder

Takefirst mage stack

Gabor filtersaplic ona"(window)

Featureextrac on"

forrepresenta on"

storing in DBsta s cal" "

descriptors

Gabor filterapplica on"

(complete image)

Select bestGabor filterparameters

End

StartFabric type

selec on (manual)"

Are theremore imagesin the stack?

Figure 3.3: Features extraction from training set.

3.2.1 Fabric recognition based on minimum distance classifiers

The statistical features defined in the previous section represents the fabrics. This section

describes the classification approach deployed to recognize the fabrics based on the

statistical features extracted. Given the discrimination capability of this representation,

a simple minimum distance based classifier is appropriate to distinguish among fabrics.


Selectimage folder

Feature

for

DB

descriptors)

Gabor filter

(complete image)

Select bestGabor filterparameters

Is the fabric type End

StartFabric type

sele (manual)c on"

Gabor filters

(window)

MeanVarianceKurtosis

Skewness

Avg. MeanAvg. VarianceAvg. Kurtosis

Avg. Skewness

YesNo

Classification of

validation data set

based on minimum

distance classifier.

aplica"on

extrac"on

representa"on

aplica"on

classified?

(sta"s"cal

Figure 3.4: Features extraction process diagram with test set (Individual).

In this approach a pattern is classified into that class whose mean is nearest to the

pattern. If two or more distances are equal the pattern is randomly assigned. By

introducing the covariance matrix Σ, the minimum Mahalanobis distance described in

equation 3.18 is suitable to classify a fabric into the categories considered since it takes

into account the correlation among features. If the covariance is the identity matrix

(identical variance), then this measure is identical to the Euclidean distance measure

[49].

dm(~x, ~y) =√

(~x− ~y)TΣ−1(~x− ~y). (3.18)

3.2.2 Preprocessing

Image preprocessing plays an important role to detect defects in fabrics. The goal of

preprocessing is to improve the quality of an image highlighting features or removing

noise. Non-uniform illumination and reflective properties of fabric makes necessary the

application of spatial corrections to improve defect detection. By applying Gamma


correction (eq. 3.19), this work removes uneven illumination caused by high reflective

properties of material surface.

Vout = AVinγ (3.19)

According to subjective visual criteria, the Gamma value that better corrects non-

uniform illumination is 2.99, this value was achieved through different visual tests in

a set of gamma values between 0 and 5.

3.2.3 Training and validation data sets

Once the features have been extracted, training and validation data sets are constructed

from image features of defective and non-defective fabrics.

The training set consists of a total of 75 images. Each of these images is convolved with

Gabor filters,then mean variance, skewness, and kurtosis are extracted.

Subsequently we calculated an average of each of the features for each of the fabrics to

form an average vector for each type of fabric of the training set. Xk = [µ, σ, sk, ku]

represents an average feature vector for fabric type k that results from averaging 75

feature vectors in the training data set. Table 3.1 presents these values.

To validate, we have a test set of 25 images, as in training dataset each image is convolved

with Gabor filters and the mean, variance, skewness, and kurtosis are extracted.

The test feature vector: Xi = [µ, σ, sk, ku] is compared against average training vectors

that represent each fabric type k: Xk = [µ, σ, sk, ku] with Mahalanobis distance. The

image Xi is classified as class k according to the shortest distance attained.

Figure 3.3 and 3.4 shows the creation of training data set and the validation process,

respectively.

Table 3.2 1 is a confusion matrix that presents validation errors for each fabric type. All

classes are classified with null error which demonstrates a good discrimination capability

of the features extracted.

1 fabrics Vulcron V. W/L and Vulcron V. N/L present the same surface and features, thus in thisvalidation the result in both cases is taken as successful with anywhere of the two cases.


Table 3.1: Statistical features for fabric representation

FabricAverage values

Mean Variance Kurtosis Skewness

Kariatyde 42.0482 32.9472 2.9538 1.5068

Vulcron V. W/L 48.5936 0.94308 2.9533 1.4196

Vulcron V. N/L 48.8488 0.89211 2.9226 1.4203

Petit P. gray 45.4331 46.9221 2.9957 1.4788

Petit P. red 46.6035 16.9522 2.8855 1.4569

Table 3.2: Confusion matrix for test identification fabrics (rows are the true class)

Kariatyde Vulcron V. W/L Vulcron V. N/L Petit P. gray Petit P. red

Kariatyde 25 0 0 0 0

Vulcron V. W/L 0 25 0 0 0

Vulcron V. N/L 0 0 25 0 0

Petit P. gray 0 0 0 25 0

Petit P. red 0 0 0 0 25

Performance 100 % 100 % 100 % 100 % 100 %

Figures 3.5, 3.6, 3.7 and 3.8 show the statistical features mean, variance, kurtosis and

skewness for kariatyde fabric. It’s possible to plot for each fabric in the feature space

the statistical features group.

0 10 20 30 40 50 60 7041

41.2

41.4

41.6

41.8

42

42.2

42.4

42.6

42.8

43

Images (training set)

Mea

n V

alue

s

Figure 3.5: Mean graph - karyatide fabric (75 images).

In this work, we propose a polar graph taken the main value (y axis) as mean, the values

of variance, kurtosis and skewness are multiplied to define the angle ϕ = [σ × sk × ku]


0 10 20 30 40 50 60 7030

31

32

33

34

35

36

37


Var

ianc

e V

alue

s

Figure 3.6: Variance graph - kariatyde fabric (75 images).

0 10 20 30 40 50 60 702.94

2.945

2.95

2.955

2.96

2.965

2.97


Kur

tosi

s V

alue

s

Figure 3.7: Kurtosis graph - kariatyde fabric (75 images).


0 10 20 30 40 50 60 701.49

1.495

1.5

1.505

1.51

1.515

1.52


Ske

wne

ss V

alue

s

Figure 3.8: Skewness graph - kariatyde fabric (75 images).

on the polar coordinates system with the objective of illustrate the distribution of the

values.

Where,

x = ρ cosϕ, y = ρ sinϕ, z = z

Figure 3.9 shows this representation.

3.2.4 Segmentation

Fabric defect detection requires image segmentation since it determines the eventual suc-

cess or failure of the automatic analysis. Segmentation separates an image into regions

of interest. In this case, the object of interest is the fabric defect. This segmentation


Figure 3.9: Final graph representation for five fabrics types (75 images by fabric).

stage is based on thresholding since it provides fast processing and speed in the indus-

trial environment. Thresholding approach provides a binary image in which ones are

displayed for all gray levels in the region of interest and zeros for all other gray levels.

3.2.5 Simple thresholding

The textures of fabrics Vinyl Vulcron WL and Vinyl Vulcron NL lack a defined orienta-

tion hence Gabor filter banks are not suitable to represent these surfaces. In contrast,

these surfaces have uniform color distributions and defects present as scratches or (sec-

tion 2.4) interruptions to this pattern of uniformity. Thresholding procedure establishes

a range of values by extracting the minimum and maximum gray level values from a

window with size 128 × 128 pixels. This window is centered on a non-defective region.

Then, those pixels in the image whose gray level is outside this range are classified as

defective. A binary image, displaying the distribution of the defects, is the output of

this procedure.

Chapter 4

RESULTS AND ANALYSIS

4.1 Description of the proposed system

A prototype is built to integrate the inspected fabric with the illumination sources, and

the camera. This structure provides an environment to test the proposed computer

vision based inspection system. Appendix B, presents the drawings of the proposed

system.

Figure 4.1 shows the flow diagram of the proposed system. First, the optical systems

is calibrated to estimate the intrinsic and extrinsic parameters. Then, user acquires a

fabric image that will be used to classify the fabric type and identify the type of defect.

4.2 Camera Calibration

4.2.1 Experimental set-up

Camera calibration is conducted on a experimental set-up with controlled illumination.

Appendix B presents the physical layout of the experimental set-up.

Fabrics samples will be placed on a uniform surface with dimensions 1.83 m by 1, 035 m.

This surface is located at a height of 0.93 m since a human operator will manipulate

the samples. Although camera diagonal field of view (FOV) covers 85% of surface area,

this coverage assures that the largest sample is included in the image acquired by the

44

Chapter 4. Results and analysis 45

Figure 4.1: General process diagram.

camera. In this experimental rig, the distance from the inspected surface to the camera

is adjustable. This distance ranges from 0.94 m to 1.94 m. The technical specifications

of the camera used can be found at [50]. Maximum area of interest and maximum

surface-camera distance are proportional.

Next, vertical and horizontal field of view (VFOV and HFOV) for FL3-GE-20S4C-C

camera are calculated from the horizontal FOV provided by the manufacturer, the cam-

era FL3-GE-20S4C-C has a camera sensor format of 11/8”, in this project the camera

is integrated with a compact Fixed Focal Length Lens of 6 mm; with these data we

know that the value of horizontal field of view is 110.3 mm - 62.3◦. Then, FOV is

experimentally measured by using visible markers on the inspected surface.

Figure 4.2 represents the camera’s field of view, rx is the width of the object, dx is the

distance from the sensor to the object and ry is the height of the object.


dh

dx

rx

dy

ry

HFOV/2α

VF

OV

/2

Figure 4.2: Geometric projections - pinhole model.

Horizontal FOV - HFOV

HFOV = 2 ∗ arctan(tan(DFOV/2) ∗ cos(arctan(4/3)))

= 2 ∗ arctan(tan(73.2/2) ∗ cos(36.87)

= 61.3◦

HFOV and VFOV determines the total FOV at a given camera-object distance.

V FOV = 2 ∗ arctan(tan(DFOV/2) ∗ sin(arctan(3/4)))

= 2 ∗ arctan(tan(73.2/2) ∗ sin(36.87)

= 48◦

Next, the area of interest obtained with HFOV and VFOV specified by the manufacturer

is calculated.

Dmax = (1.93 m)− (0.93 m) = 1.0 m

dh = dx/(cos(DFOV/2))

dh = 1, 2456 m.

dy = dh ∗ (sin(DFOV/2))

dy = 0, 7426 m


With the data obtained we can calculate the angle:

θ = arctan(3/4)

θ = 36.87◦

Lateral sizes rx and ry determine the area of interest:

rx = dy ∗ cos θ

rx = 0, 7426 m ∗ cos 36.87◦

rx = 0, 594 m

ry = dy ∗ sinα

ry = 0, 7426 m ∗ sin 36.87◦

ry = 0, 445 m.

Hline = 2 ∗ rx

Hline = 1, 18 m.

Vline = 2 ∗ ry

Vline = 0, 89 m.

With the above calculations we can ensure that the height and width of the area of

interest for lighting are 1.18 m. and 0.89 m. These results state that the sides of the

total area of interest to be captured by the camera are 1.18 m and 0.89 m. However , this

design considers an additional 5% area to be illuminated. Figure 4.3 depicts this margin

and Figure 4.4 the table’s dimensions. We also consider a margin of 10% to capture the

images, i.e the possible capacity will subtract this safety margin to minimize distortion

effects in the borders, which ultimately leaves us with an ability to inspect parts with

dimensions of 1.062 m by 0.8 m. Figure 4.4 illustrates these sizes.

Now, the area of interest to be captured by the camera is measured in the experimental

configuration.


1,41157 m.

0,7

940 m

.0,8

337 m

.1,48214 m.

a

b

Figure 4.3: a) Area calculated by trigonometric projections - Pinhole model. b)Effective area with an additional margin of 5% for lighting.

Figure 4.4: Inspection areas.


Figure 4.5 illustrates the projection camera’s optical axis on the table surface, in this

image the vertical distance from the camera to the table is 1 m. Figure 4.6 illustrate

the markers placed on table surface.

Figure 4.5: Proposed prototype from a front view.

Figure 4.6: Markers on the table.

4.2.2 Results - comparison manual method

Tables 4.1 and 4.2 compare the experimental calculations of areas of interest against

those obtained from diagonal field of view (DFOV) data provided by camera manufac-

turer. A set of 10 length and width measurements supports this examination.

Table 4.1: Values obtained by geometry - pinhole model (meters).

Theoretical measuresWidth 1,18 m.Length 0,89 m.


Table 4.2: Values obtained by direct measurement of the projection (meters)

Practical measuresTest number Width(m) Height(m)1 1.19 0.892 1.19 0.893 1.19 0.894 1.20 0.905 1.20 0.896 1.18 0.897 1.20 0.898 1.20 0.909 1.18 0.9010 1.18 0.89Average 1.191 0.893Standard deviation 0,00875 0,00483

The discrepancy between experimental calculations of areas of interest against those

obtained from DFOV data provided by camera manufacturer is 0.93% and 0.33% for

width and height respectively. This difference is adequate for reliable fabric inspection.

4.2.3 Experiments and results of calibration process.

These elements define the camera calibration process:

• Development tool: QT-Creator Version 4.7.0.

• Programming Language: C++.

• OpenCV 4.2.0.

• DB: MySQL 5.0.

• Operating System: Microsoft Windows 7.

• Flea3 FL3-GE-20S4C-C Color GigE Camera Driver.

This section describes the use of OpenCV to estimate the camera intrinsic and extrinsic

parameters. This process requires a calibration object that in OpenCV is a flat grid

of alternating black and white squares that is usually called a “chessboard” (see Ap-

pendix C). OpenCV uses multiple views of the chessboard to carry out a perspective

transform of the corners when viewed through the camera lens. The parameters for


this transform are contained in a 3 × 3 a homography matrix. The calibration pro-

cess acquires 20 views from the chessboard to obtain an error of 2%. Figure 4.7 shows

calibration pattern chessboard.

0

1 2 34

6 7 8

9 10 11 12 13 14

27 29 31 33 35

36 37 39 40 43

15 16

17 18 19 20 21 22 23 24 25

28 30 32 34

3841

42 44

45 46 47 48 49 50 51 52 53

Figure 4.7: Chessboard calibration pattern 5 x 4.

These steps describe the process followed to obtain the camera intrinsic and extrinsic

parameters:

1. Capture video streaming from the camera.

2. Capture and store chessboard distribution in still images.

3. Start camera calibration.

4. Estimate intrinsic and extrinsic parameters.

5. Presentation of results.

First, image resolution is set to 1600p x 1200p. Figure 4.8 shows the complete list of

parameters for image capture. Then, a streaming of video is acquired and the images of

the chessboard are captured and stored in a folder every 5 seconds. The program stores

the images in BMP format with a final size of 5.49 MB for each image.


Figure 4.8: Parameters for Calibration Process - (Flea3 FL3-GE-20S4C-C Color GigECamera).

To capture the parameter’s values of chess pattern, we implemented a simple interface

(see Appendix A).

4.2.3.1 Parameters of the function CalibrateCamera (OpenCV)

When calling a cvCalibrateCamera() several parameters must be defined such as:

ObjectPoints is an N × 3 matrix containing the physical coordinates of each of the K

points on each of the M images of the object. In a planar object like the chessboard we

can simply set all Z coordinates to zero.

ImagePoints is an N × 2 matrix containing the pixels coordinates of all the points

supplied in ObjectPoints.

ImageSize is the size of chessboard images extracted from the pixels.

CameraMatrix and distorsioncoeffs constitute the intrinsic parameters of the camera.


Previous two arguments summarize camera’s intrinsic information, the next two summa-

rize the extrinsic information. That is, they indicate the location of calibration objects

relative to the camera in each picture.

Rotationvectors is defined by a M three-component vectors arranged into an M − by−3

matrix where M is the number of images.

Translationvectors are similarly arranged into a second M × 3 matrix, again in the

camera coordinate system. The units of the camera coordinate system are exactly those

assumed for the chessboard.

flags argument allows finer control of exactly how the calibration will be performed.

4.2.3.2 Re-projection error estimation

The function CalibrateCamera returns the average re-projection error, which is the

results of averaging the errors of projecting image points into the object plane (i.e

chessboard) by using intrinsic and extrinsic parameters. Figure 4.9 shows the location of

chessboard pattern on the surface of the table during the calibration process. Figure 4.11

shows the calibration process results. Figure 4.10 shows the corners identified during

the calibration process.

Next, a validation procedure compares the dimensions provided by the computer vision

based system with respect to the sizes provided by a laser meter. Figure 4.12 shows the

circle and rectangle whose dimensions will be measured. The test follows these steps:

1. Generate video.

2. Capture image.

3. Prepocess the image with gaussian filtering.

4. Detect edges.

5. Measure the distance from the camera to the object through the intrinsic param-

eters estimated during the calibration process.


Figure 4.9: Sample images for the calibration process

Figure 4.10: Identification of corners during the calibration process

6. Compare results against manual measurements.

The first test consist of measuring the object-camera distance by using an object with

known dimensions. This reference object is a square with side L= 0,1 m. The reference

camera-object distance is 1 m. In these measurements, the relative error between the

mean value and the reference value is 0.46%. Table 4.3 presents mean and standard

deviation of measurement results after 10 evaluations.


Figure 4.11: Values of the parameters obtained in the calibration process

Figure 4.12: Shapes selecting for testing.

In the second test a circle diameter is measured given a known camera-object distance.

The reference circle diameter is 0.12 m. as shown in Figure 4.13, the discrepancy between

this reference value and the average of measured diameters as shown in table 4.4, is

0.03%.

Table 4.3: Measurement of the distance between the camera and the object (square).

Measurement Number Value(mm)1 991.9962 1007.4903 991.9964 992.0015 991.9966 996.6017 1001.8508 981.4969 979.80010 1018.640Average 995.387Standard deviation 11.606


Figure 4.13: Manual measurement of circle diameter D=120 mm.

Table 4.4: Measurement of circle diameter D=120 mm.

Measurement Number Value(mm)1 125.722 124.163 125.114 125.885 124.126 125.877 125.068 124.719 124.1910 124.80Average 124.962Standard deviation 0.6921


4.2.4 Evaluation of inspection methods

This section validates the proposed method to detect defects in fabrics with textures that

have either dominant directions or undefined orientations. This assessment is based on

ROC (Receiving operating characteristic) curves, which plot false positive rate against

true positive rate and account for the performance of the detection algorithm when

compared to human evaluations.

Previous works in this field such as [51] and [52] lack the information necessary to

understand the figures of merit deployed to evaluate the proposed methods. Figure 4.14

depicts a ROC curve.

Tru

e

Tru

e P

ositiv

e R

ate

50%

75%

100%

25% 50% 100%

False Positive Rate

Optimal point

Figure 4.14: ROC curve distribution.

The threshold in the ROC curve point with the highest true positive rate and lowest false

positive rate is of great interest to improve the performance of the proposed algorithm.

Hereafter, this point is named optimal point (OP).

A manual segmentation on the training data set provides the ground-truth to define the

figures of merit that measures the proposed methodology in this work, which are true

positive, false positive, and false negative. Figures 4.15a and 4.15b show a defective

fabric and the result of the manual segmentation.

ROC curves are calculated for each image extracting a AUC value from each ROC.

Therefore, AUC is the figure of merit to measure the performance of the segmentation

approaches proposed (Gabor filters and simple thresholding) on each image. 500 images


(a) Karyatide fabric defect. (b) Human (Manual) segmentation.

Figure 4.15: Kariatyde fabric defect and manual segmentation

(100 for each class) manually segmented images of fabrics with defects are the ground

truth.

4.2.5 Evaluation procedure

This section validates the proposed methodology based on ROC curves. These plots

represent the performance of the defect detection algorithm in the types of fabrics in-

spected. The area under ROC curve (AUC) summarizes in one scalar the performance

represented in each ROC curve. Figure 4.16 presents ROC curves for several images

obtained from fabric type petit point gray example, each curve corresponds to the eval-

uation of the method in a given image (4 images). In these curves each point expresses

a threshold. Appendix A shows the user interface screen to set this parameter in the

program. Figure 4.17 presents the results of the simple thresholding approach on 25 test

images. Figure 4.18 compares the defect detection provided by this approach with that

attained with manual segmentation. It is observed that AUC is greater than 0.9 in each

image and the results of automatic and manual segmentation are comparable.


0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

FP Rate

TP

Rat

e

(a) ROC Curve for petit point gray fabricimage (D001).

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

FP Rate

TP

Rat

e

(b) ROC Curve for petit point gray fabricimage (D002).

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

FP Rate

TP

Rat

e

(c) ROC Curve for petit point gray fabricimage (D003).

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

FP Rate

TP

Rat

e

(d) ROC Curve for petit point gray fabricimages (D004).

Figure 4.16: ROC curves (individuals) for petit point gray fabric.

4.3 Parameter tuning for Gabor filter banks and simple

thresholding approach

In this work, Gabor filter banks and simple thresholding are means to segment fabric

images. Figures 4.19 and 4.20 present flow diagrams that describe these approaches. The

output of each procedure is a binary image indicating the presence of defects detected.


0 5 10 15 20 250

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Defect Images

AU

C

Figure 4.17: AUC curve for images of fabric petit point red fabric (mean = 0.90876,standard deviation = 0.02488).

(a) Manual segmentation for Vinylvulcron W/L.

(b) Result of simple thresholding -Vinyl vulcron W/L.

Figure 4.18: Simple thresholding result and manual segmentation.

Even though the segmentation based on Gabor bank filters and simple thresholding

correlates well with human segmentation it is still necessary to tune parameters. For

instance, parameters such as scale and orientation in Gabor filter banks, and low and

high thresholds in simple thresholding have a wide range of possible values whose optimal

manual adjustment is difficult and cumbersome.

4.3.1 Gabor filters

Previous works estimate Gabor filter bank parameters based on the evaluation of the

highest contrast generated by convolving each filter with the same image as in [41].


Start Gaborfilter process

Select bestcombination

from bank filter

Apply Gabor

filter to defect

image

Presents Results

Mark a pixel

as defective

Is a pixel

out of theresolding

range/value?

Get central

window from

free-defect image

Compare defect

image filtered

vs

theresolding values

Presents blank

fields

for

parameters

Selection

image

path/folder

Fabric identification

End

Are theremore pixelsto evaluate?

Are thereparametersfor fabrics?

Figure 4.19: Diagram process - Gabor filters method

Figures 4.21, 4.22, 4.23 illustrate the results of convolving with an image of fabric Kary-

atide a bank of nine Gabor filters with a number of scales S=3 and orientations L=3.


Start simple

thresholding

Apply simple

thresholding

to defect image

Presents Results

Mark a pixel

as defective

A therere

more pixels

for evaluate?

Get central

window from

free-defect image

Compare defect

image filtered

vs

theresolding values

Presents blank

fields

for

parameters

A therere

parameters

for fabric?

Selection

image

path/folder

Fabric identification

End

Get thresholds

values

Yes

Is pixel (i,j)out of thresholding

range/value?

Figure 4.20: Diagram process - simple thresholding method


Figure 4.21: a)angle=0.00, frequency=0.125, α=2.449, σx=3.567, σy=0.433; b)angle=0.0, frequency=0.306, α=2.449, σx=1.456, σy=0.439; c) angle=0.0, fre-

quency=0.750, α=2.449, σx=0.594, σy=0.476


quency=0.750, α=2.449, σx=0.594, σy=0.476


quency=0.750, α=2.449, σx=0.594, σy=0.476

Gabor filter banks for defect detection in fabrics Karyatide, petit point gray, and petit

point red require the definition of a range of orientations to decide if a given pixel is

defective or non-defective. This work proposes a methodology based on genetic algo-

rithms to estimate the best set of parameters that maximizes the true positive rate of

the detection algorithm.


The variables in this process are high and low orientations, θh and θl, respectively. Fixed

values for the algorithm are kernel size, kernel angular steps and kernel frequency steps.

The main reason why we decided to fix values is that there is the prior knowledge of the

form in which the defects are manifested; primarily through lines on fabrics that have

a definite orientation. Parameter tuning is carried out on training data set, then, the

tuned parameters define a ROC curve, in which the threshold that provides the highest

true positive and the lowest false positive is selected to validate the procedure on the

test data set.

The objective function of the genetic algorithm is a threshold value φ of the average

AUC obtained from the training data set, AUC calculation is made using the trapezoid

rule.∫ b

af(x) dx ≈ 1

2

N∑

k=1

(xk+1 − xk) (f(xk+1) + f(xk)) (4.1)

The genetic algorithm starts with two parents, which means two values of θh, and two

values of θl. These are the inputs parameters:θh1, θh2, θl1, θl2

It is also necessary to compute the mutations and crossings which receive two additional

parameters that are mutation and crossover probabilities Pm and Pc.

The data set on which the genetic algorithm operates consists of 75 images for each type

of fabric. Each generation is performed by the application of the objective function on

35 randomly selected images for each pair of θh and θl values.

The genetic algorithm receives as parameters a population of two individuals, each

consisting of a value of θh and θl, and the probability of crossover Pc and mutation

probability Pc, these probabilities are validated at each generation of individuals.

To determine whether each iteration crosses or mutations are performed, a random

number between 0 and 1 is generated, if the generated value is less than Pc then a

crossover is performed, in the case of mutation is evaluated a new random number, if

this is less than Pm is executed to generate a new offspring.

Parents are converted to binary numbers to conduct the crossover and mutation oper-

ations, for mutation and crossing a random number between 1 and (l − 1) is chosen

(where l is the length of the string used to represent the individual, for crossing case the


obtained number indicates where parents are crossed, whereas for mutation the number

indicates the bit that must be mutated.

The objective function is the validation of defects with each generation of new individuals

obtained. If new individuals perform better than their parents, they become new parents

of the next generation, otherwise the cycle begins again with the previous parent. The

algorithm is executed while the evaluation result obtained parameter not exceeding the

value as a target. Figure 4.24 shows a diagram of the operation of the genetic algorithm.

4.3.2 Simple thresholding

Simple thresholding permits the segmentation of fabrics Vulcron vinyl WL and Vulcron

vinyl NL because their surfaces do not have defined orientations. The parameters for

this case are the lowest and highest gray level in a square window on a non-defective

region. To select the appropriate values of the thresholds these are changed manually

between 75% and 105% of their values, this is done in order to refine the values obtained

by thresholding process.


Figure 4.24: Diagram process - genetic algorithm.


4.4 Results

This section shows the results of parameter tuning and defect detection based on AUC

for each fabric type. In this validation, the training set is composed of images with the

prefix ”D” that range from D001 to D075 image. The training set is formed by the

set of images from D076 to D100. All images were taken with the camera described in

section 2.2 - Camera Calibration.

Table 4.5 describes the inspection process details, parameters, ROC curves, scales and

fabric images of technical details. For vulcron fabrics (vinyl W/L and vinyl N/L) the

images cover a smaller area of fabric, nevertheless; this decision is supported by the

lower computational cost of inspection method used.

D-min is the minimum value for plot ROC curves, D-max is the maximum value for plot

ROC curves and scale is the increment between D-min and D-max. Conversion factor

aids to balance the number of defective and non-defective pixels image by reducing their

sizes in the same proportion.

4.4.1 Analysis of results

Figures 4.26, 4.28, 4.30, 4.32 and 4.34 show the inspection process for training set, along

with defective and non-defective test sets. These results show that average AUC for the

test set is greater than 0.9.

4.4.1.1 Gabor filters method

For Gabor filters method, the parameter tuning with the proposed training data set

and genetic algorithm provides high AUC values and low false positive rates in images

without defects, simple thresholding results obtained with the non-defective test set are

satisfactory since they present low FP rates.

Kariatyde: evaluation of the parameters in the test set gives a true positive rate of 92%,

considering the size of the defect and its manifestation is a highly satisfactory result,

we must emphasize that the size of the images with which they worked are functionally


oriented to an automated system for detecting defects, meaning that these sizes are not

sacrificed to achieve the proposed goal.

Petit point gray: the results in terms of detection are the highest obtained with the

method of Gabor filters with 93.2% true positive rate, although the manifestation of the

defect in this fabric is visually more evident it should be noted that this can take any

shape or orientation by which the applicability of the method is demonstrated in these

conditions.

Petit point red:evaluation of the parameters in the test set gives a true positive rate of

90.9%, it is necessary to consider the fabric color is present on the surface which will

adds more complexity and computational cost image filtering.

For petit point red and petit point gray fabrics, OP reach very low values since the

defect is more evident in the inspection process without having to take high threshold

values, contrary to fabric karyatide where the defect is less noticeable so the process

takes higher OP values.

4.4.1.2 Simple thresholding

In Vulcron vinyl W/L and the Vulcron vinyl N/L fabric the detection level is reached to

90.2% and 90.3% respectively, in this case the results are essentially very similar because

the surface is the same in both fabrics. In the experiment we used different picture sizes

to analyze whether detection results improved by decreasing the object-camera distance,

but the improvement is only 0.01.


0 10 20 30 40 50 60 70 800.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

1 to 75 defect images(training set)

AU

C −

OP

− F

P r

ate

AUC−training set

OP−training set

Figure 4.25: Inspection process results - Kariatyde fabric (training set).

0 5 10 15 20 25 300.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

1 to 25 defect images(test set)

AU

C −

OP

− F

P r

ate

AUC−test set

Figure 4.26: Inspection process results - Kariatyde fabric (test set).


0 10 20 30 40 50 60 70 800.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1


AU

C −

OP

− F

P r

ate

AUC−training set

OP−training set

Figure 4.27: Inspection process results - Vulcron vinyl W/L fabric (training set).

0 5 10 15 20 25 300.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1


AU

C −

OP

− F

P r

ate

AUC−test set

Figure 4.28: Inspection process results - Vulcron vinyl W/L fabric (test set).


0 10 20 30 40 50 60 70 800.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1


AU

C −

OP

− F

P r

ate

AUC−training set

OP−training set

Figure 4.29: Inspection process results - Vulcron vinyl N/L fabric (training set).

0 5 10 15 20 25 300.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1


AU

C −

OP

− F

P r

ate

AUC−test set

Figure 4.30: Inspection process results - Vulcron vinyl N/L fabric (test set).


0 10 20 30 40 50 60 70 800.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1


AU

C −

OP

− F

P r

ate

AUC−training set

OP−training set

Figure 4.31: Inspection process results - petit point gray fabric (training set).

0 5 10 15 20 25 300.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1


AU

C −

OP

− F

P r

ate

AUC−test set

Figure 4.32: Inspection process results - petit point gray fabric (test set).


0 10 20 30 40 50 60 70 800.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1


AU

C −

OP

− F

P r

ate

AUC−training set

OP−training set

Figure 4.33: Inspection process results - petit point red fabric (training set).

0 5 10 15 20 25 300.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1


AU

C −

OP

− F

P r

ate

AUC−test set

Figure 4.34: Inspection process results - petit point red fabric (test set).


Table 4.5: Fabric images and inspection process details

Fabric Kariatyde

Inspection Method Gabor filters

Selected parameters θh = 0.967418; θl = 0.125984;

Selection method parameters Genetic algorithm

Average AUC obtained Training set 0.906; Test set 0.92

ROC-Curves D-min 0.01; D-max 1.21; scale 0.024

Conversion factor 0.3

Image size 1600 px × 1200 px

Fabric Vulcron vinyl W/L

Inspection Method Simple thresholding

Selected parameters factor = 0.966





Fabric Vulcron vinyl N/L

Inspection Method simple thresholding

Selected parameters factor = 0.941




Image size 1000 px × 1000 px.

Fabric Petit point gray








Fabric Petit point red








Chapter 5

CONCLUSIONS AND FUTURE

WORK

5.1 Conclusions

Defect detection in fabrics with Gabor bank filters reach an average measured with AUC

equivalent to 0.92 for kariatyde, petit point red and petit point gray fabrics. Furthermore

defect detection in fabrics with simple thresholding reach a average measured with AUC

equivalent to 0.902 for vulcron vinyl W/L and vulcron vinyl N/L fabrics.

Although visual evaluations of fabrics conducted by human inspectors reaches a detection

rate of 87.5%, and the average performance of automatic assessment is 91%, it not

possible to carry out a fair comparison since the fabrics and illumination conditions

in the scenarios of human and automatic evaluations are different, given that optimal

conditions in which each method is performed, human as well as the developed system

are different, lighting conditions, distance vision and view angle developed by a human

are different to that of a machine vision system.

Gabor filters and simple thresholding methods are technically successful in terms of de-

fect detection performance as well as response times. Hence, both methods can support

an automated inspection system, considering the cost of the project and true positive

rates of the inspection system developed.

75

Chapter 5. Conclusions and future work 76

Genetic algorithms are useful methods to select Gabor filter banks parameters such as

orientations. This parameter selection aids to minimize FP rate in defect detection at

fabric digital images and contributes to increased levels of true positive rates. Inspected

all fabrics, the maximum rates of true positives was 99.3% whereas the minimum value

found in false positive is 0%.

Previous works [53], [8], [54], [55] have analyzed fabrics with different textures by using

Gabor filters. They report performances of 75%, 95%, 82.86% and 100%, respectively.

The results presented in this work present a performance of 92%. This performance is

close to those reported in previous works. However, unlike previous works this document

describes thoroughly the validation process and justifies the figures of merit such as AUC.

5.2 Future Work

Automation of computer vision based fabric inspection system by implanting mech-

anisms (i.e conveyor belt) that allow automatic movement of the fabric through the

system without human intervention.

Integrate the computer vision based fabric inspection software to the information system

of the production line.

Include additional types of defects and fabrics in the set of conditions detected by the

system.

Devise methods to inspect more defects and fabrics according to Table 2.6.

Appendix A

Front system

Figure A.3 shows the user interface in which the process of fabric recognition is carried

out with the corresponding results (mean, variance, kurtosis and skewness) for an image

of kariatyde fabric. It is very important to mention that the parameters of Gabor filters

must be the same for the training and test sets (or individual fabric image).

Figure A.1: Calibrate Camera option.

77

Appendix A. User interface 78

Figure A.2: Defect inspection option.

Appendix A. User interface 79

Figure A.3: Representation (Identification fabric type) option.

Appendix B

Prototype (C.E.)

80

Appendix

B.Prototype

81

Appendix C

Developer Guide

This work was performed under the Windows 7 operating system, but it was also tested

under Windows XP and Windows 8. For correct operation, the software needs the

following applications:

QT 4.7 Development Environment.

Flea3 FL3-GE-20S4C-C Color GigE Camera Driver.

OpenCv version 2.2.

ChartDirector version 5.0.3 to C++.

MySQL Server 5.1 or higher.

The OpenCv library is already compiled, so only need to copy the folder C OpenCV2.2MinGW,

ChartDirector folder must unzip and place on C:

Installation sequence:

1. QT Development Environment and RDBMS MySQL.

2. Copy qsqlmysqld4.dll library from MySQL folder to sqldrivers folder of QT installa-

tion, i.e. C:\Qt\2014 \qt\plugins\sqldrivers.3. Create a Empty DB and import data.

4. Open project with QT.

82

Appendix C. Developer Guide 83

tbl_auc

Indexes

Constraints

set_int_id M

fab_int_id D

auc_str_filename V

auc_num_inflexion D

tbl_defect

Constraints

fab_int_id DE…

def_int_id DE…

def_str_name VA…

def_str_description VA…

def_str_state VA…

tbl_setup

Constraints

set_int_id M…

set_num_dmin D…

set_num_dmax D…

set_num_escala D…

tbl_fabric

Constraints

fab_int_id DE

fab_str_name VA

fab_str_description VA

fab_str_state VA

tbl_measure

Indexes

Constraints

fab_int_id D…

set_int_id M…

mea_str_filename V…

mea_num_escala D…

tbl_par…

tbl_settings

Constraints

fab_int_id D

stg_chr_estado V

stg_int_id D

stg_chr_rem_blanco V

stg_chr_rem_negro V

stg_int_mask_conv D

stg_int_mask_kernel D

stg_num_ang_fh D

stg_num_ang_fl D

stg_num_dmin D

stg_num_dmax D

stg_num_escala D

stg_fecha D

stg_aplica_lim_inf V

stg_aplica_lim_sup V

stg_aplica_media V

stg_aplica_gauss V

stg_aplica_ecual V

tbl_stats

Constraints

fab_int_id D…

sta_filename V…

tbl_user

Indexes

Constraints

codigo INT

FK_tbl_auc_tbl_fabric

FK_tbl_auc_tbl_setup

FK_defect

FK_tbl_measure

FK_tbl_measure_fab

FK_tbl_settings_tbl_fabric

FK_tbl_stats

Figure C.1: Entity-relation model.

Appendix

C.Develo

perGuide

84

Figure C.2: Chessboard pattern - 9x7 squares.

Appendix D

User guide

This section describes the use cases for the processes of calibration, representation, and

defect inspection.

A use case is a complete list of steps, typically defining interactions between an actor

and a system, to achieve a goal. The actor can be a human, or an external system.

In this document two actors are identified, these are the operator and the system ad-

ministrator.

85

Appendix D. User guide 86

Figure D.1: Use case camera calibrate

Use case Calibrate camera

Actors Operator (O), Capture Device (C)

Description The goal is to calibrate a digital camera and get their intrinsicand extrinsic parameters

Precondition Authenticated userShould be generating video.

Normal flow 1.O: Select the path where will be stored the images that wereused for calibration process.

O: enter the number of images for calibration

O: select the digital camera lens.

2. (C) << include >> Image capture

3. O: Start online calibration process

4. S: Calculate the intrinsic and extrinsic camera parameters.

5. S: Save calibration parameters

6. S: Load calibration parameters (front)

Alternative flow 3.a If the user selects manual calibration

3.a.1 S: Start manual calibration process

4.a If the intrinsic and extrinsic parameters are not correct

4.a.1 O: Camera calibration is rejected

4.a.2 S: Generates the message for incorrect calibration process.

4.a.3 O: Retry or ends the use case

Exceptional flow

Post-condition Camera parameters stored in the database and displayed on screen.


Figure D.2: Use case fabric representation

Use case Representation fabric

Actors Operator (O), Capture Device (C)

Description The goal is to identify a fabric from the extraction offeatures of an image and compared against data previouslyobtained from a training set.

Precondition Data obtained from the representation (training set) of all fabrics.Authenticated user

Normal flow 1. << include >> Image capture

2. S: Load Gabor filter parameters

3. O: Start individual Identification process

3. S: Feature extraction of fabric image

4. S. Compare obtained features of the current image againstthe data training set

5. S: Presenting results

6. S: Save results

Alternative flow 1. O: Select the path where will be stored the images that wereused for identification (representation) process.

1.a S: Start manual calibration process

2. O: If the parameters do not exist

2.a O: Enter/Update Gabor filter parameters

Exceptional flow

Post-condition Fabric image identified, results stored


Figure D.3: Use case Inspection (fabric defect detection process)

Use case Inspection defects fabric

Actors Operator (O), Capture Device (C), Administrator (A)

Description The goal is to identify a fabric defect

Precondition Data obtained from the Identification (training set) of all fabrics,authenticated user, identification fabric type

Normal flow 1. S: Load general parameters (Inspection and evaluation method)

2. O: Select the images path

3. O: Start individual Inspection process (Gabor Filter)

4. S: Apply filtering Gabor

5. S: Save setup data

6. S: S. Save inspection process results

7. S: Evaluate inspection method results

8. S: S. Save evaluation process results

9. S: Present results by screen

Alternative flow 3. S: Images do not exist in the path3.a S: generates an error message

8. S: if the display option is not active

8.a S: Results are not presented by screen

Exceptional flow 2. << include >> Image capture

2.a S: Inspection process is done with the images capture (online)

2.b S: No evaluation method applies.

Post-condition Fabric defect detected, results stored


Figure D.4: Use case Inspection (fabric defect detection process)

Use case Inspection defects fabric

Actors Operator (O), Capture Device (C), Administrator (A)

Description The goal is to identify a fabric defect

Precondition Data obtained from the Identification (training set) of all fabrics,authenticated user, identification fabric type

Normal flow 1. S: Load general parameters (Inspection and evaluation method)

2. O: Select the images path

3. O: Start individual Inspection process (simple theresolding)

4. S: Apply filtering simple theresolding

5. S: Save setup data

6. S: S. Save inspection process results

7. S: Evaluate inspection method results

8. S: S. Save evaluation process results

9. S: Present results by screen

Alternative flow 3. S: Images do not exist in the path3.a S: generates an error message

8. S: if the display option is not active

8.a S: Results are not presented by screen

Exceptional flow 2. << include >> Image capture

2.a S: Inspection process is done with the images capture (online)

2.b S: No evaluation method applies.

Post-condition Fabric defect detected, results stored

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