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Fundamentals of Digital Image Processing Course

This introductory course covers image representation, enhancement, compression, restoration, and more. Suitable for upper-level undergraduate computer science students. Topics include image acquisition, color representation, filtering, nonlinear operations, and feature extraction.

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Fundamentals of Digital Image Processing Course

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  1. Digital Image Processing Course Rahman Tashakkori, Appalachian Sue Lea, UNCG "A Consortium to Promote Computational Science and High Performance Computing" Workshop – July 26-28, 2004

  2. Introduction • This is an introductory course that covers fundamentals of digital image processing: • Image representation, • Enhancement, • Compression, and • Restoration. • The course is intended for upper level undergraduate students in computer science and other science majors.   • Prerequisites: Linear Algebra (Calculus II with the Instructor’s permission) and Data Structures. A course in statistics is very helpful.

  3. Introduction • Common topics discussed in an Image Processing Course: • Image acquistion and display, • Properties of the human visual system, • Color representations, • Sampling and quantization, • Point operations, • Linear image filtering and correlation, • Transforms and subband decompositions, • Nonlinear filtering,  • Contrast and color enhancement, • Dithering, and Image restoration, • Image registration, • Simple feature extraction, and • Recognition tasks.

  4. Introduction

  5. Digital Image Fundamentals Lens is made of concentric layers of fibrous cells and is suspended by fibers that attach to the ciliary body. It contains 60% -70% water, 6% fat, and more protein than any other tissue in the eye. There are 6 to 7 million cones in each eye. Cones are located at the central portion of the retina. They are highly color sensitive. We use them to resolve fine details. Cone vision is called photopic or bright-light vision. There are 75-150 million rods distributed over the retinal surface. Larger area of distribution and the fact that several of rods are connected to a single nerve, make them less effective for resolving details. Rod vision is called scotopic or dim-light vision. Focal length 17 mm to about 14 mm

  6. Grade Components Assignments There will be about 6-7 assignments or labs with postlabs Semester Project The class projects will include a wide variety of topics and will be completed individually Class Participation Due to the fact that students may receive the course remotely, class participation is extremely important Exams There will be three exams including the final. These exams must be made such that the local coordinators on each campus are able to give them without any difficulties

  7. Assignments Contents Assignments will reflect the topics covered in the course. These assignments will be available to all students via the web page of the course. Efforts should be made such that all submissions can be made electronically. For assignments which require submission of hardcopy the local and primary instructors should coordinate the submission process and the grading. Since each course have two instructors, one of the two instructors should receive the assignments and do the grading. Grading Assignments will have precise grading scale to reduce confusions and possible problems with grading, in particular, at the remote sites. Due Date All electronic submissions have a definite deadline as indicated on each assignment. Assignments which require hardcopies are due at the office of local course coordinator as indicated on the assignment.

  8. Projects Contents Semester projects should be on one of the topics discussed in the course. These projects may include developing toolkits to perform image processing techniques, improving existing applications, or solving a theoretical problem. Grading Each project will be graded for originality, amount of work, and quality of the project itself and its documentation. All students are required to submit a report for their projects. Due Date All projects are due in electronic format. Students should submit electronic versions of their projects and all their program. Presentations Time permitting, selected number of projects will be presented by students. Also, selected toolkits will be used in our instruction in future semesters.

  9. Class Participations Importance Due to the nature of this course and the way it is going to be offered both on and off campus, class attendance is extremely important. Perhaps, off campus students may be in disadvantage in such a case because they may not have access to the primary instructor to sit and discuss the details of the missing class. We should however prepare ourselves to deal with legitimate absences. That may require us to record the class sessions. I hope this does not encourage more absences. Major concern would be the breaks: fall and spring, etc … Grading In the past few semesters, I have been harsh in penalizing absences. This has really improved class attendance in all of my courses. These days, I have a very few, if any, absences for the entire semester. I have been penalizing 3 points on overall grade for each unexcused absence. It is not easy to justify an absence as an excused. I have given F to students with 8 or more absences. Extreme Circumstance Defined in my syllabus as being hospitalized or a death in the family.

  10. Exams Format Perhaps this is the hardest part of our work. Giving exams that are suitable to all students in participating campuses is not an easy task. We are thinking of giving 3 exams, including the final. These exams are paper-based, thus, the local coordinators should mail them immediately after the exam. This way the primary instructor will have a chance to grade all the exams together. Length I have always faced difficulty giving 1-hour exams in this course. There is little or no chance for giving multiple choice exams or short answer exams. Usually, students solve a few problems during the exam period. This problem might become more noticeable at the remote sites. Missing Exams and Extreme Circumstance No make up exam policy with replacement of final grade for the missing exam under extreme circumstances.

  11. Main Topics in the Textbook http://www.imageprocessingbook.com/index_dipum.htm 1. Introduction. 2. Fundamentals. 3. Intensity Transformations and Spatial Filtering. 4. Frequency Domain Processing. 5. Image Restoration. 6. Color Image Processing. 7. Wavelets. 8. Image Compression. 9. Morphological Image Processing.10. Image Segmentation.11. Representation and Description. 12. Object Recognition.

  12. 0. A Review of Linear Algebra and Statistics. Linear Algebra 0.1 Matrix Manipulation (multiplication, addition, etc…) 0.1 Transformations 0.2 Inverse 0.3 etc. Statistics 0.1 Mean 0.2 Standard division 0.3 Variance 0.4 PDF and CDF 0.5 etc.

  13. 1. Introduction. Preview 1.1 Background 1.2 What Is Digital Image Processing? 1.3 Background on MATLAB and the Image Processing Toolbox 1.4 Areas of Image Processing Covered in the Book 1.5 The Book Web Site 1.6 Notation 1.7 The MATLAB Working Environment 1.7.1 The MATLAB Desktop 1.7.2 Using the MATLAB Editor to Create M-files 1.7.3 Getting Help 1.7.4 Saving and Retrieving a Work Session 1.8 How References Are Organized in the Book Summary

  14. 2. Fundamentals. Preview 2.1 Digital Image Representation 2.1.1 Coordinate Conventions 2.1.2 Images as Matrices 2.2 Reading Images 2.3 Displaying Images 2.4 Writing Images 2.5 Data Classes 2.6 Image Types 2.6.1 Intensity Images 2.6.2 Binary Images 2.6.3 A Note on Terminology 2.7 Converting between Data Classes and Image Types 2.7.1 Converting between Data Classes 2.7.2 Converting between Image Classes and Types 2.8 Array Indexing 2.8.1 Vector Indexing 2.8.2 Matrix Indexing 2.8.3 Selecting Array Dimensions 2.9 Some Important Standard Arrays 2.10 Introduction to M-Function Programming 2.10.1 M-Files 2.10.2 Operators 2.10.3 Flow Control 2.10.4 Code Optimization 2.10.5 Interactive I/O 2.10.6 A Brief Introduction to Cell Arrays and Structures 62 Summary

  15. 3. Intensity Transformations and Spatial Filtering. Preview 3.1 Background 3.2 Intensity Transformation Functions 3.2.1 Function imadjust 3.2.2 Logarithmic and Contrast-Stretching Transformations 3.2.3 Some Utility M-Functions for Intensity Transformations 3.3 Histogram Processing and Function Plotting 3.3.1 Generating and Plotting Image Histograms 3.3.2 Histogram Equalization 3.3.3 Histogram Matching (Specification) 3.4 Spatial Filtering 3.4.1 Linear Spatial Filtering 3.4.2 Nonlinear Spatial Filtering 3.5 Image Processing Toolbox Standard Spatial Filters 3.5.1 Linear Spatial Filters 3.5.2 Nonlinear Spatial Filters Summary

  16. 4. Frequency Domain Processing. Preview 4.1 The 2-D Discrete Fourier Transform 4.2 Computing and Visualizing the 2-D DFT in MATLAB 4.3 Filtering in the Frequency Domain 4.3.1 Fundamental Concepts 4.3.2 Basic Steps in DFT Filtering 4.3.3 An M-function for Filtering in the Frequency Domain 4.4 Obtaining Frequency Domain Filters from Spatial Filters 4.5 Generating Filters Directly in the Frequency Domain 4.5.1 Creating Meshgrid Arrays for Use in Implementing Filters in the Frequency Domain 4.5.2 Lowpass Frequency Domain Filters 4.5.3 Wireframe and Surface Plotting 4.6 Sharpening Frequency Domain Filters 4.6.1 Basic Highpass Filtering 4.6.2 High-Frequency Emphasis Filtering Summary

  17. 5. Image Restoration. Preview 5.1 A Model of the Image Degradation/Restoration Process 5.2 Noise Models 5.2.1 Adding Noise with Function imnoise 5.2.2 Generating Spatial Random Noise with a Specified Distribution 5.2.3 Periodic Noise 5.2.4 Estimating Noise Parameters 5.3 Restoration in the Presence of Noise Only—Spatial Filtering 5.3.1 Spatial Noise Filters 5.3.2 Adaptive Spatial Filters 5.4 Periodic Noise Reduction by Frequency Domain Filtering 5.5 Modeling the Degradation Function 5.6 Direct Inverse Filtering 5.7 Wiener Filtering 5.8 Constrained Least Squares (Regularized) Filtering 5.9 Iterative Nonlinear Restoration Using the Lucy-Richardson Algorithm 5.10 Blind Deconvolution 5.11 Geometric Transformations and Image Registration 5.11.1 Geometric Spatial Transformations 5.11.2 Applying Spatial Transformations to Images 5.11.3 Image Registration Summary

  18. 6. Color Image Processing. Preview 6.1 Color Image Representation in MATLAB 6.1.1 RGB Images 6.1.2 Indexed Images 6.1.3 IPT Functions for Manipulating RGB and Indexed Images 6.2 Converting to Other Color Spaces 6.2.1 NTSC Color Space 6.2.2 The YCbCr Color Space 6.2.3 The HSV Color Space 6.2.4 The CMY and CMYK Color Spaces 6.2.5 The HSI Color Space 6.3 The Basics of Color Image Processing 6.4 Color Transformations

  19. 8. Image Compression. Preview 8.1 Background 8.2 Coding Redundancy 8.2.1 Huffman Codes 8.2.2 Huffman Encoding 8.2.3 Huffman Decoding 8.3 Interpixel Redundancy 8.4 Psychovisual Redundancy 8.5 JPEG Compression 8.5.1 JPEG 8.5.2 JPEG 2000 Summary 333

  20. 7. Wavelets.9. Morphological Image Processing.10. Image Segmentation.11. Representation and Description. 12. Object Recognition.

  21. Appalachian Calendar - August 3 classes at ASU … UNCG ? Rev – Review (linear algebra and statistics) UNCG has classes but no class at ASU

  22. Appalachian Calendar - September 13 classes

  23. Appalachian Calendar - October 12 classes .. Oct 11, 12, and 13 Midterm week Fall break ASU (Oct 11, and 12 UNCG) * Rahman is out of town

  24. Appalachian Calendar - November 11 classes Thanksgiving break

  25. Appalachian Calendar - December 3 classes Total: 42 classes Finals * Reading day

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