Engineering and Architecture Faculty Course Information
Computer Engineering Department
Form
Academic Year / Semester
COURSE INFORMATION
2012 – 2013 / Spring
Course Code, Name, Credits (T-A-C)
COM 480 Computer Vision (3,0,0)
Lecture and Lab
Schedules
Theoretical
Monday, 10:00 – 13:00
Lab
None
Course Type
Technical Elective
Prerequisite
Linear algebra, knowledge of probability and statistics and Object Oriented
Programming Language
Title / First and Last Name
ACADEMIC PERSONAL
Asst. Prof. Dr. Hüseyin Kusetoğulları
Room
D222
E-Mail Address / Web Page
E-mail: huseyin.kusetogullari@gediz.edu.tr
Instructor Web Page:
Course Web Page:
Telephone
0232-355 0000 – 2372
Office Hours
Monday: 15:00 – 17:00, Wednesday: 14:00 – 16:00
Assistant
None
COURSE DESCRIPTION
The course is providing all the fundamental concepts of computer vision to the undergraduate students. Topics include:
transformation of images, image processing methods such as filtering and edge detection; binary images, boundary
detection and histogram equalization; connected component analyses, tracking of the objects and motion analyses;
segmentation, clustering and decision tree algorithm.
Main Book
Lab Resources
COURSE TEXTBOOK AND OTHER MATERIALS
1. Digital Image Processing, R. C. Gonzalez and R. E. Woods, 3rd Edition, Prentice Hall, 2008.
2. Learning OpenCV, G. Bradski and A. Kaehler ,Newgen Publishing, 2008.
None
Recommended 1. Multiple View Geometry in Computer Vision, 2nd Edition, by R. Hartley, and A. Zisserman,
Cambridge University Press, 2004.
Books
2. Computer Vision: A Modern Approach, by D.A. Forsyth and J. Ponce, Prentice Hall, 2002.
WEEKLY COURSE SCHEDULE
W
D
Lec
1
11/02
Lec 1
Topics Covered
Introduction to Computer Vision and OpenCV, Cameras and Imaging.
Lab
HW
Engineering and Architecture Faculty Course Information
Computer Engineering Department
Form
2
18/02
Lec 2
Image sensing, display a picture, playing a video, image interpolation, Color models,
transformation of images and exercises using C/C++ with OpenCV
3
25/02
Lec 3
Signal and Image Processing: Binary Images, Image processing filters, Image
sampling and Interpolation methods, Image Resolution enhancement.
4
4/03
Lec 4
Image pyramids, Thresholding, Boundary detection, Image Transforms, histogram
equalization and exercises using C/C++ with OpenCV
5
11/03
Lec 5
Edge Detection techniques and Histograms: Accessing Histograms, sequences,
Prewitt, Sobel, Canny Edge detection operators and exercises.
6
18/03
Lec 6
Counter finding, matching counters, connected component analysis and exercises
7
25/03
8
1/04
Lec 7
Image Parts and Segmentation: Background Subtraction, Thresholding for
background subtraction, standard deviation for Image processing
9
8/04
Lec 8
Watershed Algorithm, Image Repair by Inpainting, Mean-Shift Segmentation
10 15/04
Lec 9
Tracking of the objects and motion: The Basics of Tracking, Corner Finding of the
objects
11 22/04
HW1
HW2
Midterm Exam: 10:00
Invariant Features, Optical Flow, Mean-Shift and Cam-shift Tracking, Exercises
HW3
12 29/04 Lec 10 Camera Models and Calibration: Camera Model, Camera Model, Undistortion
13
6/05
Lec 11 Object recognition, Machine Learning and clustering methods
14 13/05 Lec 12 Patterns and classes, feature vector, K-Means clustering and Exercises
15 20/05 Lec 13 Binary Decision Trees and Exercises
Final Exam
GRADING
HW4
Engineering and Architecture Faculty Course Information
Computer Engineering Department
Form

In order to pass the Computer Vision course - COM 480, students must show minimum competence in the exams. Any
student who does not have a weighted average of 35.0 or greater for midterm and final exams will receive an automatic
grade of FF, for lack of minimum competence.

The weighted average will be calculated as follows: (0.30 x Midterm Exams + 0.40 x Final Exam) / 0.70

Attendance: Students who fail to attend at least 70% of the classes will receive a grade of FF.

Students who meet the above requirements will have their numerical course average calculated with the following
weights:
Percent (%)
Homework
30
Midterm I
30
Final Exam
40

From the numerical course average grades, the students who meet the above requirement, letter grades ranging from
AA to FF will be determined in the usual way (taking into account overall performance and distribution of the scores,
class and effort, as well as the attendance (in class) of the student.

We will be very careful in grading the homework assignments and exams so that everybody gets the grade that he/she
deserves. Copying will not be tolerated and will be checked and punished rigorously.