ECE 438 Image Analysis & Computer Vision

Course Syllabus


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

Phone: 650-2524, 2948 e-mail:

Textbook: Digital Image Processing and Analysis: Human and Computer Vision Application with CVIPtools, 2nd 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.


  • 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%


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)






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


Image formation and sensing, CVIPlab 438_syl_files\IR_Beehive_monitor.jpg 438_syl_files\IR_coldhand_safecode.jpg



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


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


Binary image analysis


Chapter 3: pp. 104-129

Chap 3: 23-28, suppl: 1,2

Lab: Binary Object Features, parts 1,2,4, p. 133


Edge detection

Chapter 4: pp. 139-164


Lab: Edge Detection-Roberts&Sobel, p 250


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


Review and TEST #1, Study Guide, Sample test, Sample test KEY





Chapter 4: pp. 188-210

Chap 4: 22-26

Lab: Histogram Thresholding Segmentation, p. 251


Morphological filtering

Chapter 4: pp. 210-245

Chap 4: 27-30

Lab: Morphological Filters, p. 251


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



Feature extraction, shape, histogram, color, spectral, texture, using CVIPtools

Chapter 6: pp. 335-357

Chap 6: 1-6, 8, 11-17



Feature analysis, feature vectors, distance /similarity measures, data preprocessing

Chapter 6: pp. 357-368

Chap 6:18-22



Pattern classification

Face Recognition

Chapter 6: pp. 368-387

Chap 6: 23-27,30



Projects, Oral Presentations.pptx

Chapter 11: pp. 739-740



Review and TEST #2, Study Guide, Sample Test,   Sample test KEY




Presentation of term project to the class, professor and TA


Final Project paper due



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.



Possible Project Topics:

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

Suggested Project Process:

·  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

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


TOPICS - reading: Section 2.3, Chapter 11, Chapter 13, App C&D,  CVIPtools


Introduction to CVIPlab. p. 70


Image Geometry, parts 1-3, p. 132, part 4 (rotation) for extra credit


Binary Object Features, parts 1,2,4, p. 133


Edge Detection – Roberts and Sobel, p. 250




(Test #1)


Histogram Thresholding Segmentation, p. 251


Morphological Filters, p. 251, binary images only, gray scale/color for extra credit


Project proposal due. Chap 11: pp. 739-740


Work on project: application of pattern classification


Present project to the class

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  Please visit the DSS website located online at:  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


·        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)


  • 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