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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: DIPA: Computer Vision and Image Analysis, 4^th Edition,  Scott E Umbaugh, CRC Press, Taylor & Francis Group, Boca Raton, FL, 2023; Supplementary documents are available at the publisher’s web site as Support Material

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

Class Format: Two lectures and 1 lab 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 or M-files in Matlab 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 - 25%, Project - 25%

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ECE 438 LECTURE SCHEDULE

WEEK	TOPICS	READING	HOMEWORK & LAB
1	Overview, computer imaging systems, lenses	Chapter 1	Chap 1: 1-14 suppl: 1,4
2	Image formation and sensing, CVIPtools, CVIPlab	Chapter 1 Chapter 2	Chap 1: 16-18,24,27,29; suppl: 2,7 Lab: Intro CVIPlab
3	Image analysis, preprocessing	Chapter 3	Chap 3: 1,2,3,6,9,10,12,13,15,21 Lab: Image Geometry, parts 1-3
4	Binary image analysis ShoePrintForensics.pdf	Chapter 3	Chap 3: 23-28, suppl: 1,2 Lab: Binary Object Features, parts 1,2
5	Edge, Line, Shape detection	Chapter 4	Chap4:1-9,11,14,15 Labs Due
6	Edge detection performance, Hough transform, corner/shape detection	Chapter 4	Chap 4: 17-21, suppl:1,2,3,11 Lab: Edge Detection-Roberts&Sobel
7	Review and TEST #1, Study Guide, Sample test, Sample test KEY
8	Segmentation	Chapter 5	Chap 5: 1-8
9	Morphological filtering	Chapter 5	Chap 5: 9,10,11 Suppl:1,2,3 Lab: Morphological Filters
10	Segmentation evaluation methods, Intro Feature extraction, shape, histogram, color	Chapter 5&6	Chap 5 suppl: 4,5,6 Chap 6: 1-6,8 Labs Due
11	Fourier transform, Feature extraction, spectral, texture, using CVIPtools Project proposal due Must be approved by Professor	Chapter 6	Chap 6: 11-17 Project
12	Feature analysis, feature vectors, distance /similarity measures, data preprocessing	Chapter 6	Chap 6:18-21 Project
13	Pattern classification	Chapter 7	Chap 7:1-11 Project
Thanksgiving Break Holiday Week
14	Projects, Oral Presentations.pptx	See Online Docs and Chapter 2: pp. 89-90	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

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 Students will work in pairs, with a partner of their choice. Labs can be performed using CVIPlab in either C or Matlab

Ø Labs are due at the beginning of the first class period weeks 5 and 10; Late lab work is worth 50% up until 2 days late, after that it is worth 10%

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

Weeks	TOPICS - reading: Chapter 2, CVIPtools
1-5	Chapter 2: Introduction to CVIPlab. Lab1_C or Lab1_Matlab Chapter 3, p. 145: Image Geometry, parts 1-3 Chapter 3, p. 146: Binary Object Features parts 1&2
6-10	Chapter 4, p. 208: Edge Detection - Roberts and Sobel Chapter 5, p. 270: Morphological Filters, binary images only
11-15	Term project, see section 2.7, p. 89 for ideas · Create project proposal · Run experiments and analyze results · Write report and develop presentation/demo
16	Present project to the class

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.

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 You do NOT need to hand 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

General: reports should be 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.

· Title page (project title, names, course number, date, etc.)

· Table of contents with page numbers for: different sections, figures, appendices, etc.

· Abstract - 1 page or less. Concise description of what is contained in the paper.

· Introduction/Project overview

· 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.,

· Summary and conclusions. Summarize any results and draw conclusions as based on these results.

· Suggestions for future work. Include any ideas you have based on your work and conclusions about follow-up experiments and/or research.

· 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.

· Appendices - related background information, program listings, etc.

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ECE 438 Image Analysis & Computer Vision PowerPoint Lecture Slides

CVIPbook_PPLec\Chapter1_Ed4_CVIA.pptx

CVIPbook_PPLec\Chapter2_Ed4_CVIA.pptx

CVIPbook_PPLec\Chapter3_Ed4_CVIA.pptx

CVIPbook_PPLec\Chapter4_Ed4_CVIA.pptx

CVIPbook_PPLec\Chapter5_Ed4_CVIA.pptx

CVIPbook_PPLec\Chapter6_Ed4_CVIA.pptx

CVIPbook_PPLec\Chapter7_Ed4_CVIA.pptx

Homework_Lab_Solutions Manual_

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.

COVID-19 Policies Related to Classroom Instruction

Health and Safety

General Health Measures

At all times, students should engage in recommended health and safety measures, which include:

· Conducting a daily health assessment. If you have COVID-19 symptoms, but not yet tested positive, have had COVID-19 close contact exposure, or are COVID-19 diagnosed as presumptive or confirmed positive, stay home and contact your health provider or SIUE Health Service at cougarcare@siue.edu or 618-650-2842.

· Frequent washing or disinfecting of hands.

Academic Integrity

Students are reminded that the expectations and academic standards outlined in the Student Academic Code (3C2) apply to all courses, field experiences and educational experiences at the University, regardless of modality or location. The full text of the policy can be found here: https://www.siue.edu/policies/3c2.shtml.

Recordings of Class Content

Faculty recordings of lectures and/or other course materials are meant to facilitate student learning and to help facilitate a student catching up who has missed class due to illness. As such, students are reminded that the recording, as well as replicating or sharing of any course content and/or course materials without the express permission of the instructor of record, is not permitted, and may be considered a violation of the University’s Student Conduct Code (3C1), linked here: https://www.siue.edu/policies/3c1.shtml.

Brief Bibliography

Books

· 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