a robust approach for local interest point detection in line-drawing images

A Robust Approach for Local Interest Point Detection in Line-

Drawing Images

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The Anh Pham, Mathieu Delalandre, Sabine Barrat and Jean-Yves RamelRFAI group- Polytech’Tour, France.

CIL TalkWednesday 7th March 2012

Athens, Greece

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Overview

Introduction Junction detection in line-drawing images Experiments and results Conclusion and future works

Introduction (1)

This work is interested with graphic documents, especially the line drawings, some examples

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Introduction (2)

Interest points are a kind of local features (i.e. an image pattern which differs from its immediate neighborhood).

Popular interest points include edges, blobs, regions, salient points, etc. In graphics documents, interest points are end-points, corners and junctions:

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Local interest points

Approach Corner Junction RobustnessHigh curvature detection

[The-Chin89] ++++

Intensity-based methods [Harris89] ++

Model-based methods[Chul05] +++

Segmentation-based methods [Burge98] ++

Contour matching methods [Ramel00] ++

Tracking methods [Song02] ++

Comparison of the approaches for corner and junction detection

Introduction (3)5

Key idea of the work is to drive high curvature detection methods to achieve junction detection.Two problems:

(1) How to extract the curves(2) How to merge the multiple detections

High curvature detection is the task of segmenting a curve at distinguished points of high local curvature (e.g. corners, bends, joints).

High curvature detection methods often includes include polygonal and B-splines approximation, wavelet analysis, etc.

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Overview

Introduction Junction detection in line-drawing images Experiments and results Conclusion and future works

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Junction detection in line-drawing images (1)

Flow-work of our approach

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Junction detection in line-drawing images (2)

Skeletonization, branch linking

High curvature detection

Path extraction

1D signals

Skeleton graph

Path representation

2D paths

Refining & Correcting

Candidates

(1) Skeletonization based on Di Baja (3,4)-chamfer distance [DiBaja94]

(2) Branch linking and Skeleton Connective Graph Construction (SCG) based on [Popel02]

Skeleton Connective Graph (SCG):

node: ended and crossing points

edge: skeleton branch

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Junction detection in line-drawing images (3)

Path definition: a sequence of edges of SCG that describes a complete stroke or a circuit.Three types of paths: Stroke path, Circuit path and Hybrid path.

Paths are extracted using anticlockwise direction between the nodes of graph SCG:

A skeleton graph

A stroke path A circuit path

Skeletonization, branch linking

High curvature detection

Path extraction

1D signals

Skeleton graph

Path representation

2D paths

Refining & Correcting

Candidatesd0

are branch pixelsare branch extremitiesis a crossing pixel

d0 is the extremity-crossing direction

Junction detection in line-drawing images (4)

Skeletonization, branch linking

High curvature detection

Path extraction

1D signals

Skeleton graph

Path representation

2D paths

Refining & Correcting

Candidates

A 2D path P consists in N points: (x1y1), (x2y2),…,(xNyN) To represent a 2D path in 1D signal, we selected the Rosenfeld-Johnston method:

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pi-q

pi pi+q

tqa

tqb

tqtq

tqtq

ba

baar

cos

,, qttqtttq yyxxa

,, qttqtttq yyxxb

tqtq

tqtq

ba

batf

)cos()(

pI-q pI pI+qtqa

tqb

f(t)straight-line -1high curvature

/2 0 pI-q pI

pI+q

tqa

tqb

Junction detection in line-drawing images (5)

Skeletonization, branch linking

High curvature detection

Path extraction

1D signals

Skeleton graph

Path representation

2D paths

Refining & Correcting

Candidates

Due to the q parameter, we must make the method shift invariant.To do so, we select starting point of lowest curvature i.e. f(t)-

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A good starting point here (shift-invariant).

Not goodstarting point.

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Skeletonization, branch linking

High curvature detection

Path extraction

1D signals

Skeleton graph

Path representation

2D paths

Refining & Correcting

Candidates

Using multi-resolution wavelet analysis because of its robustness and scale invariance (i.e. multi-resolution)[Gao06].

Junction detection in line-drawing images (6)

1D representationImage (I) 2D curcuit path

Multi-resolution wavelet analysis

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(1) Single path level: Remove the “unreliable” segments (i.e. length less than line thickness) and Connect the “reliable” segments togethers.

(2) Inter-path level (using voting scheme): merging close junctions together based on line thickness.

Junction detection in line-drawing images (7)

Skeletonization, branch linking

High curvature detection

Path extraction

Skeleton graph

Path representation

2D paths

Refining & Correcting

Candidates

1D signals

a path with high curvature points

a SCG with high curvature points

result after removing short segments

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Overview

Introduction Junction detection in line-drawing images Experiments and results Conclusion and future works

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Experiments and Results (1)

Evaluation protocol:Evaluation Criteria is the repeatability score [Schmid00]

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2

p is a model pointq is a detected point

pq

Detection of p ispositive if d(p,q)<with d(p,q) the Euclidean distance

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Experiments and Results (2)

Datasets:

Logos-UMD ISRC2011Models 106 150Degradation

Rotation + Scaling + Kanungo noise

Test images 1272 3600

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Experiments and Results (3)

Some results

+ Liu99: “Identification of Fork point on the Skeletons of Handwritten Chinese Characters”, PAMI (1999).+ Haris detector: “A combined corner and edge detector”. Alvey Vision Conference, (1988).

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Experiments and Results (4)

Some visual results

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Overview

Introduction Junction detection in line-drawing images Experiments and results Conclusion and future works

Conclusions and future works

Conclusions: A junction detector is proposed for line-drawing images The obtained results are rather promising

Future works The method is threshold dependent, we are looking for threshold adaptation

(e.g. region of support Improve the robustness of the merging step using topological analysis

(e.g. line bending energy minimization) More experiments with more interest points detector and datasets Applications of recognition of spotting (logos, symbols) and image indexing

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Thank you for your attention!