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ECE 438 Image Analysis & Computer Vision

Course Syllabus

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Professor: Dr. Scott E Umbaugh Office: Engineering Building, Room EB3037

Phone: 650-2524, 2948 e-mail: sumbaug@siue.edu

Textbook: Digital Image Processing and Analysis: Human and Computer Vision Application with CVIPtools, 2^nd Edition, SE Umbaugh, CRC Press, 2011

Prerequisite: ECE 351 and programming experience, or consent of instructor

Class Format: Two lectures and 1 lab/homework per week, two tests, term project

Web Site Imaging Examples: CVIPtools Imaging Examples , Computer Vision Example Applications

Goals and Objectives: To introduce the student to computer vision algorithms, methods and concepts which will enable the student to implement computer vision systems with emphasis on applications and problem solving. Lab exercises will familiarize the student with typical hardware as well as software development tools. Students will use the C programming language to implement computer vision algorithms.

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OUTLINE

• Introduction and Image Sensing 4 Lectures
• Image Analysis 4 Lectures
• Edge/Line Detection 4 Lectures
• TEST #1
• Segmentation/Morphological Filtering 4 Lectures
• Fourier Transform 2 Lectures
• Feature Extraction/Analysis 4 Lectures
• Pattern Classification 2 Lectures
• TEST #2
• PROJECT DUE -- 16th week

Project will be some application of computer vision to digital image(s). Typical projects are simple pattern classification applications using CVIPtools libraries.

GRADING: Test #1 - 25%, Test #2 - 25%, Lab Exercises & Homework - 25%, Project - 25%

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ECE 438 LECTURE SCHEDULE

Ø Homework is due at the beginning of the first class period the week after assigned; 5 points are earned for working all the problems and one homework problem will be randomly selected from each set to be graded for up to 5 more points (see late policy below)

WEEK	TOPICS	READING	HOMEWORK & LAB
1	Overview, computer imaging systems, lenses	Chapter 1: pp. 3-13, Chapter 2: pp.15-27	Chap 1: 1-6 Chap 2: 1-4, 6-12, suppl: 1,4
2	Image formation and sensing, CVIPlab 438_syl_files\IR_Beehive_monitor.jpg 438_syl_files\IR_coldhand_safecode.jpg DisplayRGB_Patterns.pdf CMOS_vs_CCD	Chapter 2: pp. 28-68, Chapter 11: pp. 715-739, Chapter 13: p. 855	Chap 2:13-15,21,25,27; suppl: 2,7 Lab: Intro CVIPlab, p.70
3	Image analysis, preprocessing	Chapter 3: pp. 77-104	Chap 3: 1,2,3,6,9,10,12,13,15,21 Lab: Image Geometry, parts 1-3, p. 132
4	Binary image analysis ShoePrintForensics.pdf	Chapter 3: pp. 104-129	Chap 3: 23-28, suppl: 1,2 Lab: Binary Object Features, parts 1,2,4, p. 133
5	Edge detection	Chapter 4: pp. 139-164	Chap4:1-9,11,14,15 Lab: Edge Detection-Roberts&Sobel, p 250
6	Edge detection performance, Hough transform, corner detection	Chapter 4: pp. 164-188	Chap 4: 17-21, suppl:1,2,3 Not collected due to test
7	Review and TEST #1, Study Guide, Sample test, Sample test KEY
8	Segmentation	Chapter 4: pp. 188-210	Chap 4: 22-26 Lab: Histogram Thresholding Segmentation, p. 251
9	Morphological filtering	Chapter 4: pp. 210-245	Chap 4: 27-30 Lab: Morphological Filters, p. 251
10	Fourier transform Project proposal due Must be approved by Professor	Chapter 5: pp. 259-282 Chapter 11: pp. 739-740	Chap 5: 1-10, suppl: 1,2 Project
11	Feature extraction, shape, histogram, color, spectral, texture, using CVIPtools	Chapter 6: pp. 335-357	Chap 6: 1-6, 8, 11-17 Project
12	Feature analysis, feature vectors, distance /similarity measures, data preprocessing	Chapter 6: pp. 357-368	Chap 6:18-22 Project
13	Pattern classification Face Recognition	Chapter 6: pp. 368-387	Chap 6: 23-27,30 Project
14	Projects, Oral Presentations.pptx	Chapter 11: pp. 739-740	Project
15	Review and TEST #2, Study Guide, Sample Test, Sample test KEY
16	Presentation of term project to the class, professor and TA Final Project paper due

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ECE 438 Image Analysis & Computer Vision - Semester Project

Semester Project: The project will consist of designing experiments, implementing algorithms, and analyzing the results for a computer vision problem. You will work with a partner. You will get the images for your project by using the cameras in the CVIP lab or your own camera – part of the project is image acquisition. The project will be selected by the students, subject to approval by the professor. The project proposal, due week 10, will include: 1) classes to be identified, 2) number and type of images to be used, 3) potential algorithm(s) for object extraction, 4) classification method(s) to be used, 5) method of evaluation of results. For the image sets, a minimum of four classes and ten images of each class is recommended. In this case, five of each class can be used for training and five for testing.

New Project 2017: TV Dinner Identification

A paper will be written describing the project and discussing what was learned during the project. The final paper should be about 10 to 15 pages, typed and double-spaced; include images ! In the paper include an appendix containing related data files and/or program listing(s). The students will give a short presentation of their project in the lab to the class, the professor, and the lab instructor. These presentations will take place during the scheduled final exam period, and will be 5 minutes in length. Do NOT go over 5 minutes and do not have more than 10 PowerPoint slides! Also, an evaluation for each group member will be handed in or emailed with the report.

Ø In addition to handing in a paper copy of the report, email me a soft copy of the Word file. Before you send me the file give it a meaningful name that includes your last name(s) and the project title.

Grading: The project is worth 25% of your term grade, broken down as follows:

• Overall Project.. 15%
• Paper................. 5%
• Presentation........ 5%

NOTES: 1) Start on your project as early in the term as possible, 2) late projects are worth 0.

Project Paper Format Outline

• 1. Title page (project title, names, course number, date, etc.)
• 2. Table of contents with page numbers for: different sections, figures, appendices, etc.
• 3. Abstract - 1 page or less. Concise description of what is contained in the paper.
• 4. Introduction/Project overview
• 5. Body of paper. Broken down into sections as required for you project. For example: Background/theory, experimental methods, discussion and analysis of results, program descriptions, etc. Present results using graphs, images, etc.,
• 6. Summary and conclusions. Summarize any results and draw conclusions as based on these results.
• 7. Suggestions for future work. Include any ideas you have based on your work and conclusions about follow-up experiments and/or research.
• 8. References. Be sure your references are complete. Avoid web sites as references – these come and go – find the source, which is usually a published paper.
• 9. Appendices - related background information, program listings, etc.

General: reports should be typed, double spaced, pages numbered starting with abstract. Number of pages?- do what is necessary, but keep it concise, extra stuff can go in an appendix. DO NOT put in plastic folder, simply staple in upper left hand corner.

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Possible Project Topics:

• 1) Recognition of geometric shapes, e.g. circles, squares, rectangles, triangles, etc.
• 2) Classification of different tools, e.g. screwdrivers, wrenches, hammers, etc.
• 3) Identification of different coins and bills.
• 4) Design a program to read bar codes.
• 5) Character recognition.
• 6) Tableware classification for robot
• Other projects of your choice with emphasis on applications of pattern recognition/classification

Important Note: Your project needs to work with real world input, i.e. the camera or scanner. You need to design it to be robust, i.e. to handle various sizes, orientations, lighting conditions, etc. If it cannot handle these you need to define a calibration routine that is easy and fast. As part of the demo we will test the robustness via having the program classify "unknown" input.

Week What is due

• 10 (or sooner) Brief, 1 page, project proposal
• 16 Final project paper due short demonstration to professor and lab instructor

Suggested Project Process:

• 1) Define the problem
• 2) Design experiment(s) you wish to pursue
• 3) Design algorithms/C function(s) to implement related to project, or use CVIPtools
• 4) Code and debug your function(s)
• 5) Test your functions on some real images
• 6) Process images/do the experiments
• 7) Compare and contrast your results to other similar results from using CVIPtools functions, or research results in library from similar experiments - Analyze results using appropriate metrics, tabulate or plot, etc.
• 8) Write report, include images
• 9) Demonstration to the class
• For more details on using CVIPtools with your project, go here

· NOTE: If you do not have any specific images that you want to use, take a look at the image databases on the Internet, such as: DIP Image Databases

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ECE 438 Image Analysis and Computer Vision Laboratory Outline

Ø Homework and program listings will be handed in at the beginning of the first class period the week after assigned

Ø Late homework and lab work is worth 50% up until 2 days late, after that it is worth 10%

Ø Students can do their labs using CVIPlab in either C or Matlab

Ø Useful document for those familiar with C++, but not C programming: C for C++ Programmers.htm

Week	TOPICS - reading: Section 2.3, Chapter 11, Chapter 13, App C&D, CVIPtools
1&2	Introduction to CVIPlab. p. 70
3	Image Geometry, parts 1-3, p. 132, part 4 (rotation) for extra credit
4	Binary Object Features, parts 1,2,4, p. 133
5	Edge Detection – Roberts and Sobel, p. 250
6	(Study)
7	(Test #1)
8	Histogram Thresholding Segmentation, p. 251
9	Morphological Filters, p. 251, binary images only, gray scale/color for extra credit
10	Project proposal due. Chap 11: pp. 739-740
11-15	Work on project: application of pattern classification
16	Present project to the class

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ECE 438 Image Analysis & Computer Vision PowerPoint Lecture Slides

CVIPbook_PPLec/Chapter1_ed2.pptx

CVIPbook_PPLec/Chapter2_ed2.pptx

CVIPbook_PPLec/Chapter3_ed2.pptx

CVIPbook_PPLec/Chapter4_ed2.pptx

CVIPbook_PPLec/Chapter5FFT.ppt

CVIPbook_PPLec/Chapter6_ed2.pptx

Class Attendance Policy: Based on University Class Attendance Policy 1I9: It is the responsibility of students to ascertain the policies of instructors with regard to absence from class, and to make arrangements satisfactory to instructors with regard to missed course work. Failure to attend the first session of a course may result in the student’s place in class being assigned to another student.

Class Policies:  If you have a documented disability that requires academic accommodations, please go to Disability Support Services for coordination of your academic accommodations. DSS is located in the Student Success Center, Room 1270; you may contact them to make an appointment by calling (618) 650-3726 or sending an email to disabilitysupport@siue.edu.  Please visit the DSS website located online at: www.siue.edu/dss for more information.

Students are expected to be familiar with and follow the Student Academic Code. It is included in the SIUE Policies and Procedures under Section 3C2.2.

Brief Bibliography

Books

• Computer Imaging: Digital Image Analyis and Processing , Scott E Umbaugh, The CRC Press, Boca Raton, FL, January 2005

· 1a. Computer Vision and Image Processing: A Practical Approach Using CVIPtools - S. E Umbaugh, Prentice Hall PTR, Upper Saddle, NJ, 1998

• 1. Digital Image Processing - R.C.Gonzalez & P.Wintz
• 2. Robot Vision - B.K.P.Horn
• 3. Computer Vision - D.H.Ballard & C.M.Brown
• 4. Syntactic Pattern Recognition : An introduction -R.C.Gonzalez and M.G.Thomason
• 5. Pattern Recognition - A Statistical Approach - P.A. Devijver and J. Kittler
• 6. Digital Image Processing - W. K. Pratt
• 7. Fundamentals of Digital Image Processing - A.K. Jain
• 8. Digital Picture Processing - A. Rosenfeld and A.C. Kak
• 9. Pattern Classification and Scene Analysis - R.O. Duda and P.E. Hart
• 10. Object Recognition by Computer - W.E.L. Grimson
• 11. Digital Pictures - A.N. Netravali and B.G. Haskell
• 12. Vision in Man and Machine - M.D. Levine
• 13. Pattern Recognition Statistical, Structural and Neural Approaches, R.J Schalkoff, John Wiley & Sons NY
• 14. Digital Image Processing and Computer Vision, R.J. Schalkoff, Wiley
• 15. Artificial Intelligence: An Engineering Approach, R.J. Schalkoff, McGraw-Hill
• 16. Algorithms for Graphics and Image Processing, Theo Pavlidis, Computer Science Press, call no.: T385.P381982
• 17. Handbook of Pattern Recognition and Image Processing, K.S. Fu and T.Y. Young, Academic Press
• 18. The Image Processing Handbook, John C. Russ, CRC Press SIUE Library call #: TA1632.R881992 (reference)

Journals

• 1. IEEE Transactions on Pattern Analysis and Machine Intelligence
• 2. IEEE Transactions on Computers
• 3. Pattern Recognition
• 4. Computer Vision, Graphics and Image Processing
• 5. IEEE Transactions on Medical Imaging
• 6. Computerized Medical Imaging and Graphics
• 7. IEEE Transactions on Image Processing
• 8. IEEE Engineering in Medicine and Biology
• 9. IEEE Transactions on Signal Processing
• 10. IEEE Transactions on Neural Networks
• 11. IEEE Transactions on Geoscience and Remote Sensing
• 12. Photogrammetric Engineering and Remote Sensing
• 13. International Journal of Remote Sensing
• 14. Journal of Visual Communication and Image Representation

Numerous Conference Proceedings from the following professional groups:

• IEEE - Institute of Electrical and Electronic Engineers
• SPIE - Society of Photographic and Instrumentation Engineers, The International Society for Optical Engineering
• SMPTE - The Society of Motion Picture and Television Engineers
• PRS - Pattern Recognition Society