Computer Engineering Department, Koç University
Office: Eng139 Phone: (0212) 338 1585
E-mail: yyemez@ku.edu.tr
Tuesday & Thursday 12:30-13:45, Eng-B05
ECOE 508 is a graduate course to introduce the fundamentals of computer vision theory and practice. With recent developments in computing, transmission and display technologies, 2D/3D visual data are becoming more commonplace in scientific, industrial and commercial arenas. The digital visual data are mostly reflections from real world and contain useful information. The main goal of computer vision is to analyze sensed images for extracting this information, to construct scene descriptions and knowledge representations, to recognize objects and thereby to make useful decisions about physical objects and scenes.
The course is open to graduate students who are willing to understand the vision technology in conjunction with real world applications and especially very well suited to those who are interested in doing research in computer vision. Good programming skills and knowledge of C/C++ are necessary for the course project and homework assignments. Basic DSP knowledge will also be very helpful.
Syllabus (in pdf)
Textbook
M. Sonka, V. Hlavac and R. Boyle, Image Processing, Analysis, and Machine Vision, Thomson-Engineering, 1998.
Reference books
Shapiro and Stockman, Computer Vision, Prentice Hall, 2001.
E. Trucco and A. Verri, Introductory Techniques for 3-D Computer Vision, , Prentice Hall, 1998.
Honor Code
All code and documentation handed in exams, assignments and projects must be your own work. In programming assignments, you can exchange ideas, but you should not ever share your code, even partly.
Grading
Final grades will be composed of: (tentative)
|
Programming assignments |
30% |
|
Exam
|
30% |
|
Project |
40% |
There will be programming assignments posted here. All code must be written either in C/C++ using OpenCV libraries or Matlab Image Processing Toolbox. Source code documentation and organization should make your programs easy to read and convey your understanding of the implemented applications. Poor documentation and programming style will result in a lower score.
Assignment 1, due to February 20, Wednesday.
Assignment 2, due to March 3, Monday.
Assignment 3, due to March 17, Monday
Assignment 4, due to April 4, Friday
By the first month of the semester, each student will have chosen a topic for her/his project. Projects can be either research oriented or applications programming oriented, addressing one of the computer vision problems/concepts/applications covered throughout the course. Depending on the chosen topic, students may be expected to do a literature survey on different techniques aiming at solving the specified problem and then to implement and test one of these techniques. A software implementation is mandatory, using C/C++/OpenCV or Matlab.
Students are expected to submit a project proposal by April 15.
| Lecture | Topic | Reading |
| Feb 7 | Imaging | Chapters 1 and 2 |
| Feb 12-21 | Filtering & Enhancement | Chapter 3, Chapter 5.1, 5.2 |
| Feb 26-28 | Fetaure Detection | Chapter 5.3, Chapter 3.2 |
| March 4,6,11,13 | Pattern Recognition Concepts | Chapters 8 and 9 |
| March 18,20 | PCA & LDA | See supplementary notes |
| March 25 | Texture | Chapter 15 |
| March 27 | Image retrieval | Shapiro/Chapter 8 |
| April 1,3,15 | Segmentation | Chapter 5 |
| April 22,24, May 6 | 3D Vision | Chapters 9,10 |
| May 8 | Binary Morphology | Chapter 11.1-11.5 |
Supplementary reading that may be helpful:
Segmentation as graph partitioning
Exam Date: 13 May, Tuesday, Class hours
Project Presentations: 28 May, Thursday, 13:30-15:30
OpenCV Resources
OpenCV Manual (pdf file, a bit out of date, but useful; look into \OpenCV\docs\index.htm file for a complete documentation once you install OpenCV)
An image processing tool for Windows (similar to Photoshop)