ele882 intrintroduction to digital image …courses/ele882/slides/lecture...1 ele882...

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ELE882

Introduction to Digital Image ProcessingIntroduction to Digital Image Processing

Course Instructor:Prof. Ling Guan

Department of Electrical & Computer EngineeringDepartment of Electrical & Computer EngineeringRoom 315, ENG BuildingRoom 315, ENG Building

Tel: (416)979Tel: (416)979--5000 ext 60725000 ext 6072Email: [email protected]: [email protected]

Co-Instructor & TA:Muhammad Talal Ibrahim

Department of Electrical & Computer EngineeringDepartment of Electrical & Computer EngineeringRoom Room 426, 426, ENG BuildingENG Building

Tel: (416)979Tel: (416)979--5000 ext 5000 ext 61066106Email: [email protected]: [email protected]

ELE 882: Introduction to Digital Image Processing (DIP)

Lecture time/room Tue: 5-6pm (ENGLG05)Thu: 8-10am (ENGLG13)

Lab time/room Mon: 2-3pm (ENG409)Text books and notes 1. R. C. Gonzalez and R. E. woods, “Digital Image Processing”, 3rd

di i P Ed i I 2008edition, Pearson Education, Inc., 2008. 2. “Digital Image Processing using MATLAB” R. C. Gonzalez , R. E.

Woods and S.L. Eddins Pearson Education, Inc., 2008.3. Class Slides

Additional books Milan Sonka, Vaclav Hlavac and Roger Boyle, “Image Processing, Analysis, and Machine Vision ”, 3rd edition, Thomson-Engineering, 2007.

T. Svoboda , J. Kybic and V. Hlaváč , “Image Processing, Analysis, and Machine Vision: A MATLAB Companion”, Thomson-

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Engineering, 2007.

Scott E Umbaugh, Digital Image Processing and Analysis: Human and Computer Vision Applications with CVIPtools, 2nd edition, CRC

Press, 2010.

Prerequisites 1. Knowledge of Vectors and Matrices.2. Working knowledge of MATLAB3. Signals and Systems course especially the concepts of Convolution,

Fourier Transform, filtering, etc.

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Grading Policy

Midterm: ~20%

Quizzes: ~10%Q

Assignments (written + programming) ~15%

Lab Experiments/Project ~15%

Final: ~40%

Grading policy can change without notice during the semester

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in benefit of all the students

Lecture notes will be available at the course websitehttp://www.ee.ryerson.ca/~courses/ele882

Quizzes, Midterm and Counseling Hours

Quizzes

Thursday, January 27 Scheduled Quiz

Thursday, February 10 Scheduled Quiz

Monday, Feb 15 or Thursday, Feb 17 Surprise Quiz

Thursday, March 3 Midterm

Thursday, March 17 Scheduled Quiz

Thursday, March 31 Scheduled Quiz

Monday, Apr 5 or Thursday, Apr 7 Surprise Quiz

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Counseling Hours

Monday, Room No. : ENG 426 9:00 am to 10:00 am

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Assignments

Please check the Blackboard system every day, for the notification of assignments, projects and other updated information.

Assignments will have ~15% weight in the total marks.

Assignments may be written or programming.

There will be a total of around 6 to 8 assignments.

The deadline for the submission of assignment will be given with the assignment.

Assignments submitted after the deadline will not be accepted and

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Assignments submitted after the deadline will not be accepted and will carry ZERO MARKS.

Cheated assignments will get ZERO MARKS.

Project

Projects will have ~10% weight in the total marks. Projects may be conducted individually or in groups of two

students. S t d j t t i ill b l d d t th Blackboard Suggested project topics will be uploaded to the Blackboard

system within the first two weeks of the course. Reading material and other sources for every project to help the

students will also be given. If you want to do your own project take permission first. Project topics should be selected and approved within the first five

weeks of the course

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weeks of the course. Project presentation date will be announced and projects will not

be accepted after the presentation date. Projects consisting of Downloaded codes or presentations will not

be accepted and will carry ZERO MARKS.

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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 phoneSend an image through mobile phone

Enhance and restore images– Remove scratches from an old photo– Improve visibility of tumor in a radiograph

Extract information from images– Measure water pollution from aerial images

M th 3D di t d h i ht f bj t f t

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

Prepare for display or printing– Adjust image size– Halftoning

Image Processing Examples

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Photo restoration

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Damaged Image Restored Image


Photo colorization

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Original B/W Image colorized Image Original Image Colorized Image

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Color photo enhancement

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Original Images Enhanced Images


Halftoning

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Restoration of image from Hubble Space Telescope

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Faulty image of Saturn Recovered image


Extraction of settlement area from an aerial image

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Degraded Image Noise-reduced Image

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Earthquake analysis from space

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


Medical Imaging: Computer Tomography (CT) – Generating 3-D images from 2-D slices.

– CAD, CAM applications, pp

– Industrial inspections

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Medical Imaging: Computer Aided Tomography (CAT)

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Medical Imaging: Ultrasound imaging

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Medical imaging: Averaging MRI slices for knee image

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Image compression

Original JPEG 27:1

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Image compression

Original JPEG2000 27:1

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Face detection

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Face Tracking

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Face Morphing

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Fingerprint recognition

X X

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Applications of DIP

Categorization according to image sources

Electromagnetic (EM) band ImagingElectromagnetic (EM) band Imaging

– Gamma ray images

– x-ray band images

– ultra-violet band images

– visual light and infra-red images

– Imaging based on micro-waves and radio waves

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g g

Non-EM band Imaging

– Acoustic and ultrasonic images

– Electron Microscopy

– Computer-generated synthetic images

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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,

Vi ibl & I f d b d i i ( t id l d)

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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

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Applications of DIP

Non-EM band imaging

Acoustic imaging (hundreds of Hz)– Geological exploration (oil exploration)

Ultrasound imaging (millions of Hz)– Industry and medicine especially in obstetrics, determine the health

of the fetal development

Electron microscopic imaging

– Used to achieve magnification of 10,000x or more

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• (Light microscopy is limited to around 1000x)

Synthetic imaging– 3D modeling or visualization systems for flight simulators, machine

design, special effects and animations,etc.


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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 g p p genhancement, image sharpening, etc.

Mid-level process (DIP and Computer Vision and Pattern Recognition)

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

Hi h l l (C t Vi i )

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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

Image acquisition

Physical world

ImagingImage acquisition

Digitization, quantization and compression

Enhancement and restoration

Image segmentation

Feature selection/extraction

Image Processing

Imaging Analysis(Computer Vision and P tt iti )

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Image representation

Image interpretation

Physical action

Pattern recognition)

Image understanding(Computer Vision and Pattern recognition)

Image Processing Computer vision and PR

Image acquisition by sensor Image sampling and quantization

Image Geometrical Rectification Camera geometryFeature Extraction Edge and Interest points detection Image enhancement and restoration

Filtering in spatial domain or

Com

p

Texture and shading Shape from texture and shadingCalculation on Multiple Views Multi-view geometry and Stereo imaging Structure from motion Segmentation Impose some order on group of pixels to

separate them from each otherl hi

Filtering in spatial domain or frequency domain

Feature Extraction Edge detection Interest pointsColored image Processing Pseudo coloring Color segmentationMulti resolution analysis

puter Vision

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Template matchingMulti-resolution analysis Pyramids Wavelets Other transformationsImage and video compression Image compression standards Video compression standards

SegmentationClassification and Recognition Classification and interpretation of objects

based on selected features Recognize objects using probabilistic

techniques

Pattern R

ecognition

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Scope of DIP Course Digital image fundamentals and image acquisition (briefly)

Image enhancement in spatial domain – pixel operations– histogram processingg p g– Filtering

Image enhancement in frequency domain – Transformation and reverse transformation– Frequency domain filters– Homomorphic filtering

Image sampling Image restoration

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Image restoration – Noise reduction techniques– Geometric transformations

Color image processing – Color models – Pseudocolor image processing– Color transformations and color segmentation

ele882 intrintroduction to digital image …courses/ele882/slides/lecture...1 ele882...

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