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: Application with MATLAB and CVIPtools, 3rd Edition, SE Umbaugh, Taylor&Francis/CRC Press, 2018

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.

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%

ECE 438 LECTURE SCHEDULE

Ø Labs are due at the beginning of the first class period the week after assigned; see late policy below

WEEK

TOPICS

READING

HOMEWORK & LAB

1

Overview, computer imaging systems, lenses

Chapter 1: pp. 3-11, Chapter 2: pp.13-23

Chap 1: 1-6

Chap 2: 1-4, 6-12, suppl: 1,4

2

Image formation and sensing, CVIPlab

Chapter 2: pp. 24-76, Chapter 11: pp. 631-669

Chap 2:13-15,21,25,27; suppl: 2,7

Lab: Intro CVIPlab

3

Image analysis, preprocessing

Chapter 3: pp. 69-93

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: pp. 93-113

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

Lab: Binary Object Features, parts 1,2,3,4

5

Edge detection

Chapter 4: pp. 121-144

Chap4:1-9,11,14,15

Lab: Edge Detection-Roberts&Sobel

6

Edge detection performance, Hough transform, corner detection

Chapter 4: pp. 144-165

Chap 4: 17-21, suppl:1,2,3,11

Not collected due to test

7

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

 

 

8

Segmentation

Chapter 4: pp. 165-182

Chap 4: 22-26

Suppl:4,6

Lab: Histogram Thresholding Segmentation

9

Morphological filtering

Chapter 4: pp. 182-213

Chap 4: 27-30

Suppl:9,10

Lab: Morphological Filters

10

Fourier transform

Project proposal due

Must be approved by Professor

Chapter 5: pp. 225-248

Chapter 11: pp. 669-672

Chap 5: 1-10, suppl: 1,2

Project

11

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

Chapter 6: pp. 295-317

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

Project

12

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

Chapter 6: pp. 317-326

Chap 6:18-22

Project

13

Pattern classification

Face Recognition

Chapter 6: pp. 326-345

Chap 6: 23-27,30

Project

14

Projects, Oral Presentations.pptx

Chapter 11: pp. 669-672

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

 

 

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  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 Lab reports will be handed in at the beginning of the first class period the week after assigned

Ø Late 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, Appendices,  CVIPtools

1&2

Chapter2: Introduction to CVIPlab.

3

Chapter 3: Image Geometry, parts 1-3, part 4 (rotation) for extra credit

4

Chapter 3: Binary Object Features, parts 1,2,3,4, part 5 for extra credit

5

Chapter 4: Edge Detection – Roberts and Sobel

6

(Study)

7

(Test #1)

8

Chapter 4: Histogram Thresholding Segmentation

9

Chapter 4: Morphological Filters,  binary images only, gray scale/color for extra credit

10

Project proposal due. Chap 11

11-15

Work on project: application of pattern classification

16

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 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 Pandemic Policies Related to Classroom Instruction (Fall 2020)

Health and Safety

Consistent with the Illinois Board of Higher Education guidance contained in “Safely Launching Academic Year 2020” released on June 23, 2020 and guidelines established by Governor J. B. Pritzker and Restore Illinois, Southern Illinois University Edwardsville has implemented a new policy to help ensure the safety of all students, faculty and employees during the pandemic. The measures outlined below are required and any student who does not comply may be in violation of the COVID-19 People-Focused Health and Safety Policy, as well as the University’s Student Code of Conduct. 

The full text of the COVID-19 People-Focused Health and Safety Policy can be found here:  https://www.siue.edu/policies/Covid.shtml.

Classrooms, Labs, Studios, and Other Academic Spaces

 

While in the classroom, lab, studio, or other academic spaces, students shall practice social distancing measures by maintaining a distance of at least six feet from others in the classroom and wearing a face covering. Extra care should be taken upon entering and leaving the classroom spaces. Classroom furniture should not be rearranged, and furniture that has been taped off or covered should not be used.

 

Students who forget to wear a face mask or face shield will be reminded of their obligation to comply with SIUE’s COVID-19 People-Focused Health and Safety Policy and temporarily asked to leave the class until they are able to conform to the policy.  Students who forget or lose their face coverings may be able to obtain replacements from a friend, a faculty member, or a nearby departmental office. Face coverings are also available for purchase in the Cougar Store (MUC). 

 

Students who refuse to wear a face covering will be asked to leave the classroom and referred to the Dean of Students for non-compliance with community health and safety protocols.  Repeated non-compliance may result in disciplinary actions, including the student being administratively dropped from an on-ground/face-to-face course or courses without refund if no alternative course format is available.

If a student has a documented health condition which makes wearing a face covering medically intolerable, that student should contact ACCESS to explore options with the understanding that ACCESS will not grant accommodations which excuse the need for a face covering while on campus or in the classroom.  ACCESS will work with qualifying individuals to find reasonable alternatives, whenever such solutions are available. Please call or contact the ACCESS Office via email to schedule an online appointment to discuss potential alternatives.  ACCESS office (Student Success Center, Room 1203, 618-650-3726, and myaccess@siue.edu).

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.  More information is available on the SIUE COVID-19 website.

·         Frequent washing or disinfecting of hands.

·         Social distancing by maintaining a distance of at least six feet from others.

·         Face masks or face coverings that cover the nose and mouth are required in indoor public spaces regardless of the ability to maintain social distance. Indoor public spaces include common spaces or community settings that anyone can access, such as reception areas with walk-in access, restrooms, hallways, classrooms, teaching and research laboratories, as well as common spaces in residence halls, conference rooms, lobbies, and break rooms.

·         Adhere to directional signs and traffic flow patterns in buildings and offices.  Doors for entering and exiting buildings will be designated. Where multiple doors exist, in and out doors will be marked with “Entrance” and “Exit” signs.
Plans that consider traffic flow in and out of buildings, and within buildings (i.e. stairs, hallways, etc. where possible) will be marked. 

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.

Potential for Changes in Course Schedule or Modality

As the COVID-19 pandemic continues, there remains a possibility that planned classroom activities will need to be adjusted.  Depending on circumstances and following state-issued recommendations, potential changes include changes in course modality (e.g., transition from face-to-face to online) or in course scheduled meetings.  These changes would be implemented to ensure the successful completion of the course.  In these cases, students will be provided with an addendum to the class syllabus that will supersede the original version.

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