Introduc Introduc Intr troduction to oducti tion t tion to Digi - - PDF document

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E E LE LE 882 882 Introduc Introduc Intr troduction to oducti tion t tion to Digi n to Di Digit gital I ital Im tal Image P l Image age Pr e Proc Process ocessin ocessing essing ing Course Instructor: Prof. L ing Gua n

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E LE 882 E LE 882

Intr Introduc

  • ducti

tion t n to Di Digit ital Im l Image age Pr Process

  • cessin

ing Introduc troduction to tion to Digi gital I tal Image P e Proc

  • cessing

essing

Course Instructor:

  • Prof. L

ing Gua n

Department of Electrical & Computer Engineering Department of Electrical & Computer Engineering Room 315, ENG Building Room 315, ENG Building Tel: (416)979 Tel: (416)979-

  • 5000 ext 6072

5000 ext 6072 Email: lguan@ee.ryerson.ca Email: lguan@ee.ryerson.ca

Co-Instructor & TA:

Muha mma d T a la l Ibra him

Department of Electrical & Computer Engineering Department of Electrical & Computer Engineering Room Room 426, 426, ENG Building ENG Building Tel: (416)979 Tel: (416)979-

  • 5000 ext

5000 ext 6106 6106 Email: m28ibrah@ee.ryerson.ca Email: m28ibrah@ee.ryerson.ca

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 2008 edition, 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 MATLAB 3. 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 website

http://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 phone Send 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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Image Processing Examples Image Processing Examples

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Image Processing Examples

Photo restoration

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Damaged Image Restored Image

Image Processing Examples

Photo colorization

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Original B/W Image colorized Image Original Image Colorized Image

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Image Processing Examples

Color photo enhancement

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Original Images Enhanced Images

Image Processing Examples

Halftoning

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Image Processing Examples

Restoration of image from Hubble Space Telescope

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Faulty image of Saturn Recovered image

Image Processing Examples

Extraction of settlement area from an aerial image

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Degraded Image Noise-reduced Image

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Image Processing Examples

Earthquake analysis from space

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

Image Processing Examples

  • Medical Imaging: Computer Tomography (CT)

– Generating 3-D images from 2-D slices. – CAD, CAM applications , pp – Industrial inspections

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Image Processing Examples

  • Medical Imaging: Computer Aided Tomography (CAT)

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Image Processing Examples

  • Medical Imaging: Ultrasound imaging

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Image Processing Examples

Medical imaging: Averaging MRI slices for knee image

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Image Processing Examples

Image compression

Original JPEG 27:1

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Image Processing Examples

Image compression

Original JPEG2000 27:1

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Image Processing Examples

Face detection

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Image Processing Examples

Face Tracking

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Image Processing Examples

Face Morphing

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Image Processing Examples

Fingerprint recognition

X

X

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Applications of DIP

Categorization according to image sources

  • Electromagnetic (EM) band Imaging

Electromagnetic (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

  • f 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.

Image Processing Examples

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Image Processing Examples Image Processing Examples

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Image Processing Examples Image Processing Examples

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Image Processing Examples Image Processing Examples

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Image Processing Examples

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 g enhancement, 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

  • bjects

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 Imaging Image 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 geometry

Feature Extraction

  • Edge and Interest points detection

Image enhancement and restoration

  • Filtering in spatial domain or

Comp

  • Texture and shading
  • Shape from texture and shading

Calculation 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 other

l hi

  • Filtering in spatial domain or

frequency domain

Feature Extraction

  • Edge detection
  • Interest points

Colored image Processing

  • Pseudo coloring
  • Color segmentation

Multi resolution analysis puter Vision

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Template matching Multi-resolution analysis

  • Pyramids
  • Wavelets
  • Other transformations

Image and video compression

  • Image compression standards
  • Video compression standards

Segmentation Classification and Recognition

  • Classification and interpretation of objects

based on selected features

  • Recognize objects using probabilistic

techniques

Pattern Recognition

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Scope of DIP Course

  • Digital image fundamentals and image acquisition (briefly)
  • Image enhancement in spatial domain

– pixel operations – histogram processing g 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