ELEC 5638 Digital Image Processing

Transcription

1 ELEC 5638 Digital Image Processing University of Colorado Denver College of Engineering and Applied Science Department of Electrical Engineering Term: Spring 2011 Meeting: Monday/Wednesday 6:30pm-7:45pm Professor Yiming Deng Website: Web material will be posted at BlackBoard (ELEC 5638): NOTE: All communication by students must use ucdenver.edu as the domain, s from gmail, hotmail, yahoo, etc are NOT considered valid methods of communication. Course Design Catalog Description: Basics of two-dimensional (2-D) systems theory, including 2-D Fourier transform, Z-transform, and difference equations. Design of 2-D filters for image processing applications. Image transforms, including the 2-D FFT, cosine, Hadamard and Kl. Image enhancement and restoration techniques. Methods of image coding and compression. Instructor Description: Digital pictures today are all around us, on the web, on DVDs, and on digital satellite systems, for example. In this course we will investigate the creation and manipulation of digital images by computer. The course will consist of theoretical material introducing the mathematics of images and imaging, as well as computer exercises designed to introduce methods of real-world data manipulation using the powerful MATLAB. Topics will include representation of two-dimensional data, time and frequency domain representations, filtering and enhancement, the Fourier transform, convolution, interpolation, color images, and techniques for animation. Prerequisites: The background for this course is ELEC 5637 Digital Signal Processing Topic Prerequisites: Knowledge of linear algebra, basic probability and statistics, introductory knowledge of basic programming language, MATLAB/C are preferred Course Relationship to Program Outcomes: Students attain: ability to apply knowledge of mathematics, science and engineering ability to function on multi-disciplinary teams ability to identify, formulate, and solve engineering problems understanding of professional and ethical responsibility ability to communicate effectively recognition of the need for, and ability to engage in, life-long learning knowledge of contemporary issues ability to use the techniques, skills, and modern engineering tools necessary for engineering practice 1

2 Course Objectives: The course objectives include: overview of digital image processing field; understand the fundamental DIP algorithms and implementation; gain experience in applying image processing algorithms to real problems. Course Outcomes: Demonstrated understanding of the basic concepts of two-dimensional signal acquisition, sampling, and quantization. Demonstrated understanding of spatial filtering techniques, including linear and nonlinear methods. Demonstrated understanding of 2D Fourier transform concepts, including the 2D DFT and FFT, and their use in frequency domain filtering. Demonstrated understanding of the Human Visual System (HVS) and its affect on image perception and understanding. Demonstrated understanding of the fundamental image enhancement algorithms such as histogram modification, contrast manipulation, and edge detection. Demonstrated programming skills in digital image processing related problems Demonstrated teamwork and communication skills through course projects Course Topics Introduction and Fundamentals of DIP, Chapters 1&2 Enhancement: Transforms and Spatial Filtering, Chapter 3 Enhancement: Frequency domain Filtering, Chapter 4 Restoration and Reconstruction, Chapter 5 Morphological image processing, Chapter 10 Image compression, Chapter 9 Image Segmentation, Chapter 11 Course Policies: Required textbook: Digital Image Processing using MATLAB, 2nd ed., Gonzalez/Woods/Eddins, publisher: Gatesmark Publishing ISBN-13: , In addition, there are course reference materials that will be provided. Reference textbook: R. C. Gonzales and R. E. Woods, Digital Image Processing, 3rd ed., Prentice-Hall. W. K. Pratt, Digital Image Processing, John Wiley K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall J. S. Lim, Two Dimensional Signal and Image Processing, Prentice-Hall. Additional, Materials, Equipment: IEEE Transactions on Image Processing, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Medical Imaging Assessment Designs Grades are as follows: (* the following final grades with corresponding raw score intervals are just for reference. Your final grade will be assigned according a curve. That is, your raw score will be adjusted with respect to the class performance) A Superior/Excellent, % 2

3 B Good/Better than Average, 75 84% C Competent/Average, 65 74% D Minimum Passing, 60 64% F Failing (25 %) Exam (mid-term) (30 %) Final Project: (final project abstract/proposal due one month after the first class) Technical merits (15%), Communication skills, e.g. presentation, slides, Q&A (5%), Final report (10%) (40 %) Assignments (5 %) Class Attendance *Note that borderline scores can be pushed up by demonstration of effort. Incomplete grades will be given only in unusual cases of illness or other personal emergency which causes the student to miss a significant amount of the course. This grade cannot be given for any other reason. Assignments and Examinations: Examinations are intended to measure your individual mastery of the material. Exams concentrate on your understanding of the important concepts, rather than your ability to memorize details. All major examinations will be held in class with exact dates determined in class. The exams will generally test your knowledge of assignment material, so you are responsible for mastering all lab, homework, and programming material submitted with other partners, as if you did all the work by yourself. All exams will be close books and close notes. Assignments: There are 10 homework assignments in total, and the lowest score out of the total assignments will be dropped. The problems to be graded will not be determined until after the homework submission. Posting of new assignments will be announced in class. Homework is due one week after each assignment during the class (usually on Monday) and the solutions will be posted online right after that. LATE HOMEWORK WILL BE GRADED AS ZERO POINTS. If you need permission to postpone the homework submission, please talk to the instructor AT LEAST 5 DAYS before the deadline with appropriate reasons with proof/document signed by appropriate personal. Homework solutions must be original copies in student s own handwriting, except for MATLAB assignments. All MATLAB original codes should be submitted with results. Solutions must be clear and neatly written to receive credit. The final homework grade will be calculated as: HOMEWORK FINAL GRADE = SUM{9 out of 10 homework scores}/9 Lecture: Lecture material (slides and notes) will be made available on the web prior to class. Lecture will also consist of chalk drawings, overhead drawings, and content not explicitly present in slides and notes. Attendance: Student attendance is taken each class, beginning the second week. The students class attendance will be considered in the final grading, if the student is found to miss the lectures without appropriate reasons for over TWO times, the full 5% credit will be taken off. Course Policies: Policies regarding class attendance, turning in late work, missing homework, tests or exams, make-ups, requesting extensions, reporting illnesses, cheating and plagiarism, changes to 3

4 the syllabus. Academic policies will be consistent with the University's polices at the College of Engineering and Applied Science's website: Extensions/make-ups: A make up exam will be GIVE ONLY IN LEGITIMATE CASES OF ILLNESS OR PERSONAL EMERGENCY WHICH IS DOCUMENTED BY A PHYSICIAN OR OTHER APPROPRIATE OFFICAL. A student who finds it necessary to miss an exam and/or take a make up exam should contact the instructor before the exam to explain the circumstances. In general, late work will not be accepted. Turn in all work by the established deadline. In case you have difficulties finishing an assignment contact the instructor before the deadline. Late work can be accepted only under circumstances beyond student's control and after arrangement with the Instructor, prior to the deadline. Note: work turned-in on time is eligible for partial credit. It will always be better to turn work in by the deadline, as trying to ``perfect'' it and turn it in late will give you no points at all. You have to follow the submission and media policies and guidelines published on the web. Plagiarism is the passing of someone else's work as one's own, without giving the original author due credit. Scholastic dishonesty will be treated very strictly as per University of Colorado rules. Students with disabilities requiring accommodations, please contact the Office of Disability Resources & Services located in NC #2514 phone , TTY The staff will assist you in both determining reasonable accommodations as well as coordinating these accommodations. Students called for military duty-if you are a student in the military with the potential of being called to military service and /or training during the course of the semester, you are encouraged to contact your school/college. Course Resources: The students can use the computer labs for course assignments and projects. Unfortunately, no outside university support will be offered to install the software on the student s home system. Tentative Course Schedule Week Concepts 1 Introduction to Digital Image Processing 2 Preliminary Knowledge of Math and MATLAB Programming Fundamentals of Digital Image Processing 3 Intensity Transformations and Spatial Filtering 4 Spatial Filtering and Applications * Final project proposal is due 5 Filtering in the Frequency Domain 6 Frequency Filtering Appications 7 Midterm Exam (checkpoint) 8 Image Restoration and Reconstruction 9 Image Restoration and Reconstruction 4

5 10 Morphological Image Processing 11 Morphological Image Processing (cont d) Image Compression 12 Image Compression 13 Image Segmentation 14 Image Segmentation 15 Course summary and final assessment. Final Project Presentation 5