Digital Image Processing (DIP)Dr. Abdul Basit Siddiqui

Assistant Professor-FURC

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Digital Image Processing (DIP)

Instructor Dr. Abdul Basit Siddiqui Text Book R. C. Gonzalez and R. E. Woods, “Digital Image

Processing, Pearson Education, Inc., 2002. Prerequisites 1. Fundamental knowledge of probability and

random variables, Vectors and Matrices. 2. Working knowledge of Matlab 3. DSP topics such as convolution, FFT,

filtering, etc.

Yahoo Group Lectures and Assignments will be updated on yahoo group regularly.

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Grading Policy

Attendance 05%

Assignments 05%

Quizzes 05%

Project 05%

Midterm 30%

Final 50%

History

• 1921: Image transmission

– Newspaper industry– Cable transmission– London – New York

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History

• 1960’s: Space program

– Moon picture– Enhancement by

computer • 1970: Computerized

tomography (CT)

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• The first picture of the moon by a U.S. spacecraft on July 31,1964 at 9:09 A.M. (courtesy of NASA)

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Why Do We Process Images?

Facilitate picture storage and transmission

– Efficiently store an image in a digital camera

– Send an image through mobile phone

Enhance and restore images

– Remove scratches from an old photo

– Improve visibility of tumor in a radiograph

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Why Do We Process Images?

Extract information from images – Measure water pollution from aerial images

– Measure the 3D distances and heights of objects from stereo images

Prepare for display or printing – Adjust image size

– Halftoning

Image Processing Applications– Nuclear medicine– Medical Diagnostics– Automated Industrial Inspection– Remote Sensing

• Weather Prediction• Military reconnaissance

– Geological exploration– Astronomical Observations– Image database management– The paperless office– Photographers, advertising agencies and publishers– Machine vision– Biometrics

• Finger Prints• Iris etc.

– Movies and entertainment

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Image Enhancement

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Image Processing Examples

Photo Restoration

Damaged Image Restored Image

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Image Processing ExamplesPhoto Restoration

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Image Processing Examples

Photo Colorization

Original B/W Image Colorized Image Original Image Colorized Image

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Image Processing Examples

Photo Colorization

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Image Processing Examples

Original Images Enhanced Images

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Image Processing Examples

Restoration of Image from Hubble Space Telescope

Faulty Image of Saturn Recovered Image

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Image Processing Examples

Halftoning

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Image Processing Examples

Halftoning

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Image Processing Examples

Halftoning

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Image Processing Examples

Extraction of Settlement Area from an Aerial image

Faulty Image of Saturn Recovered Image

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Image Processing Examples

Earthquake Analysis from Space

Image shows the ground displacement of a typical area due to earthquake

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Image Processing ExamplesStereo Images from Satellite

Image shows the ground displacement of a typical area due to earthquake

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Image Processing Examples

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Image Processing ExamplesFace Detection

Image shows the ground displacement of a typical area due to earthquake

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Image Processing Examples

Face Tracking

Image shows the ground displacement of a typical area due to earthquake

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Image Processing ExamplesFace Morphing

Faulty Image of Saturn Recovered Image

Image Morphing

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Image Processing Examples

Fingerprint Recognition

Faulty Image of Saturn Recovered Image

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Applications of DIP

Electromagnetic (EM) band Imaging– Gamma ray band images

– X-ray band images

–Ultra-violet band images

– Visual light and infra-red images

– Imaging based on micro-waves and radio waves

– Some Research ProjectsSome Research Projects

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Monitoring Human Behavior from Video Taken in an Office Environment

• A system which makes context-based

decisions about the actions of people in a

room. These actions include entering,

using a computer terminal, opening a

cabinet, picking up a phone, etc.

• Source: Source: http://server.cs.ucf.edu/~vision/http://server.cs.ucf.edu/~vision/

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EM Spectrum

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Applications of DIP (EM Band Imaging)

Gamma-Ray Imaging

– Nuclear medicine, astronomical observations.

X-Ray Imaging– Medical diagnostics (CAT scans, x-ray scans), industry, astronomy.

Ultra-Violet Imaging

– Fluorescence microscopy, astronomy

Visible & Infrared-band Imaging (most widely used)– Light microscopy, astronomy, remote sensing, industry, law enforcement, military recognizance, etc.

Micro-wave and Radio band Imagery

– Radar, Medicine (MRI), astronomy

MONITORING HEAD/EYE MOTION FOR DRIVER ALERTNESS

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MONITORING FAST FOOD PRODUCTION

• The purpose of the project is to automatically monitor a fast food employee as she puts together a sandwich. Helpful in determining correctness of sandwich assembly, collecting statistics on employee performance and food safety inspection.

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Classification of DIP and Computer Vision Processes

Low-Level Process: (DIP)

– Primitive operations where inputs and outputs are images; major functions: image pre-processing like noise reduction, contrast enhancement, image sharpening, etc.

Mid-Level Process (DIP and Computer Vision)

– Inputs are images, outputs are attributes (e.g., edges); major functions: segmentation, description, classification / recognition of objects

High-Level Process (Computer Vision)

– Make sense of an ensemble of recognized objects; perform the cognitive functions normally associated with vision

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Image Processing Steps

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DIP Course

Digital Image Fundamentals and Image Acquisition (briefly)

Image Enhancement in Spatial Domain– Pixel operations– Histogram processing– Filtering

Image Enhancement in Frequency Domain– Transformation and reverse transformation– Frequency domain filters– Homomorphic filtering

Image Restoration– Noise reduction techniques

– Geometric transformations

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DIP Course

Color Image Processing– Color models

– Pseudocolor image processing

– Color transformations and color segmentation

Wavelets and Multi-Resolution Processing

– Multi-resolution expansion

– Wavelet transforms, etc.

Image Compression

– Image compression models

– Error free compression

– Lossy compression, etc

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DIP Course

Image Segmentation

– Edge, point and boundary detection

– Thresholding

– Region based segmentation, etc

Image Representation

• Image– Two-dimensional function f(x,y)– x, y : spatial coordinates

• Value of f : Intensity or gray level

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Digital Image

• A set of pixels (picture elements, pels)

• Pixel means– pixel coordinate– pixel value– or both

• Both coordinates and value are discrete

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Example

• 640 x 480 8-bit image

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