55:247 Image Analysis and Understanding

Course Syllabus, Spring 2014

Instructor:

Milan Sonka
4016 SC
Phone: (319)-335-6052
e-mail: milan-sonka@uiowa.edu
Office hours: individually arranged due to project-oriented nature of the course

Textbook:

M.Sonka, V. Hlavac, R. Boyle: Image Processing, Analysis, and Machine Vision, Cengage Learning 2014, 4th edition

References:

A. K. Jain: Fundamentals of Digital Image Processing, Prentice Hall, 1989 edition.
Marr, D.: Vision, Freeman, 1982 edition
Niemann, H.: Pattern Analysis and Understanding, Springer Verlag, 1990 edition
Wechsler, H.: Computational Vision, Academic Press, 1990 edition

Forsyth, D., Ponce, J.: Computer Vision - A Modern Approach, Prentice Hall; ; ISBN : 0130851981, 2002, see also:http://www.cs.berkeley.edu/~daf/book.html and http://www.cs.berkeley.edu/~daf/book3chaps.html

Goals:

This course represents an advanced course of digital image analysis and understanding. The student's ability to mathematically analyze two-dimensional images is extended to higher level of processing image data. As a novel concept for students, knowledge, heuristic, and fuzzy information are introduced as possible approaches to deal with uncertainty in situations where image data do not contain sufficient information. A computer-engineering approach to solving complex image analysis problems is developed. Each student is required to complete a laboratory project consisting of a sequence of image analysis steps resulting in image interpretation thus emphasizing hands-on image analysis experience.

Syllabus

I will frequently assign reading for the next class from the accompanying textbook. During the class, I will briefly go over the material and will in detail explain parts that were not clear, and/or discuss new approaches not covered in the book. Here is a suggestion of topics that may be covered - I will be open to your suggestions if you want me to cover some other topic, just let me know.

8. Shape representation and description

• read chapter 8, it will only be covered very briefly in class

7. Segmentation II

9. Object recognition

• (read statistical and NN approaches in book if you did not take Pattern Recognition)
• syntactic PR will be covered

10. Image understanding

11. 3D vision, geometry, and radiometry

12. Use of 3D vision

15. Texture

16. Motion analysis