ELE 882: Introduction to Digital Image Processing (DIP)
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1 ELE882 Introduction to Digital Image Processing Course Instructor: Prof. Ling Guan Department of Electrical & Computer Engineering Room 315, ENG Building Tel: (416) ext Co-Instructor & TA: Muhammad Talal Ibrahim Department of Electrical & Computer Engineering Room 426, ENG Building Tel: (416) ext 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 Additional books edition, i Pearson Education, Inc., Digital Image Processing using MATLAB R. C. Gonzalez, R. E. Woods and S.L. Eddins Pearson Education, Inc., Class Slides Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis, and Machine Vision, 3rd edition, Thomson- Engineering, T. Svoboda, J. Kybic and V. Hlaváč, Image Processing, Analysis, and Machine Vision: A MATLAB Companion, Thomson- Engineering, Scott E Umbaugh, Digital Image Processing and Analysis: Human and Computer Vision Applications with CVIPtools, 2nd edition, CRC Press, Prerequisites 1. Knowledge of Vectors and Matrices. 2. Working knowledge of MATLAB 3. Signals and Systems course especially the concepts of Convolution, 1/18/2011 Fourier Transform, filtering, etc. 2 1
2 Grading Policy Midterm: ~20% Quizzes: ~10% Assignments (written + programming) ~15% Lab Experiments/Project ~15% Final: ~40% Grading policy can change without notice during the semester in benefit of all the students Lecture notes will be available at the course website 1/18/ Quizzes, Midterm and Counseling Hours Quizzes Thursday, January 27 Thursday, February 10 Monday, Feb 15 or Thursday, Feb 17 Thursday, March 3 Thursday, March 17 Thursday, March 31 Monday, Apr 5 or Thursday, Apr 7 Scheduled Quiz Scheduled Quiz Surprise Quiz Midterm Scheduled Quiz Scheduled Quiz Surprise Quiz Counseling Hours Monday, Room No. : ENG 426 9:00 am to 10:00 am 1/18/
3 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 will carry ZERO MARKS. Cheated assignments will get ZERO MARKS. 1/18/ Project Projects will have ~10% weight in the total marks. Projects may be conducted individually or in groups of two students. Suggested project ttopics will be uploaded d 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. 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. 1/18/
4 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 Extract information from images Measure water pollution from aerial images Measure the 3D distances and heights ht of objects from stereo images Prepare for display or printing Adjust image size Halftoning 1/18/
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6 Photo restoration Damaged Image Restored Image 1/18/ Photo colorization Original B/W Image colorized Image Original Image Colorized Image 1/18/
7 Color photo enhancement Original Images Enhanced Images 1/18/ Halftoning 1/18/
8 Restoration of image from Hubble Space Telescope Faulty image of Saturn Recovered image 1/18/ Extraction of settlement area from an aerial image Degraded Image Noise-reduced Image 1/18/
9 Earthquake analysis from space Image shows the ground displacement of a typical area due to earthquake 1/18/ Medical Imaging: Computer Tomography (CT) Generating 3-D images from 2-D slices. CAD, CAM applications Industrial inspections 1/18/
10 Medical Imaging: Computer Aided Tomography (CAT) 1/18/ Medical Imaging: Ultrasound imaging 1/18/
11 Medical imaging: Averaging MRI slices for knee image 1/18/ Image compression Original JPEG 27:1 1/18/
12 Image compression Original JPEG :1 1/18/ Face detection 1/18/
13 Face Tracking 1/18/ Face Morphing 1/18/
14 Fingerprint recognition X X 1/18/ Applications of DIP Categorization according to image sources Electromagnetic (EM) band Imaging Gamma ray images x-ray band images ultra-violet band images visual light and infra-red images Imaging gbased on micro-waves and radio waves Non-EM band Imaging Acoustic and ultrasonic images Electron Microscopy Computer-generated synthetic images 1/18/
15 EM Spectrum 1/18/ 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 ibl & Infrared-band d imaging i (most widely used) Light microscopy, astronomy, remote sensing, industry, law enforcement, military recognizance, etc. Micro-wave and radio band imagery Radar, Medicine (MRI), astronomy 1/18/
16 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 (Light microscopy is limited to around 1000x) Synthetic imaging 3D modeling or visualization systems for flight simulators, machine design, special effects and animations,etc. 1/18/
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20 Classification of DIP and Computer Vision Processes Low-level process: (DIP) Primitive operations where inputs and outputs are images Major functions: image pre-processing p like noise reduction, contrast 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 objects High-level l process (Computer Vision) i make sense of an ensemble of recognized objects; perform the cognitive functions normally associated with vision 1/18/
21 Image Processing Steps Physical world Image acquisition Digitization, quantization and compression Enhancement and restoration Image segmentation Feature selection/extraction Image representation Image interpretation Physical action Imaging Image Processing Imaging Analysis (Computer Vision and Pattern recognition) Image understanding (Computer Vision and Pattern recognition) 1/18/ Image Processing Computer vision and PR Image acquisition by sensor Image sampling and quantization Image enhancement and restoration Filtering in spatial domain or frequency domain Feature Extraction Edge detection Interest points Colored image Processing Pseudo coloring Color segmentation Multi-resolution analysis Pyramids Wavelets Other transformations Image and video compression Image compression standards Video compression standards Image Geometrical Rectification Camera geometry Feature Extraction Edge and Interest points detection 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 Template matching Segmentation Classification and Recognition Classification and interpretation of objects based on selected features Recognize objects using probabilistic techniques 1/18/ Comp puter Vision Pattern Recognition 21
22 Scope of 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 sampling Image restoration Noise reduction techniques Geometric transformations Color image processing Color models Pseudocolor image processing Color transformations and color segmentation 1/18/
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