ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, PHD)
PhD	TR-NQF-HE: Level 8	QF-EHEA: Third Cycle	EQF-LLL: Level 8

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
EEE6531	Image Understanding	Fall	3	0	3	12

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi ZAFER İŞCAN
Recommended Optional Program Components:	None
Course Objectives:	The aim of the course is to study advanced image processing computer vision concepts, which tries to “make computers see and interpret” using the observations in the form of multiple 2D (or 3D) images and video. The course will provide the participants with a background in computer vision both in practical aspects as being able to implement computer vision algorithms, and their mathematical understanding.

Learning Outcomes

The students who have succeeded in this course;
1. Discuss the main problems of advanced computer vision, its uses and applications
2. Design various advanced feature extraction methods.
3.Describe advanced segmentation approaches involving e.g. markov random fields, graph based methods.
4.Define and compute structure from motion using direct methods
5.Describe structure from motion using factorization
6.Discuss multibody motion segmentation algorithms
7.Discuss shape from shading
8.Apply stereo vision principles
9.Describe 2D object recognition approaches
10.Design face recognition algorithms

Course Content

Extraction of optimal Edge, Shape and Skeleton Features. Hough Transform, Segmentation 1 - Markov Random Fields, Filtering Methods, Graph based Methods, Overview of Camera Models, Calibration and Optimization Theory, MRF and Simulated Annealing, Particle Filters/Sequential Important Sampling (SIS), PF Methods for Tracking, Structure From Motion - Early approaches, Structure From Motion - Filtering based approaches, Optical Flow estimation, Factorization, Multibody motion segmentation, Shape from Shading, Stereo Vision, 2D object recognition, Face Recognition

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Extraction of optimal Edge, Shape and Skeleton Features. Hough Transform.
2)	Segmentation - Markov Random Fields, Filtering Methods, Graph based Methods
3)	Overview of Camera Models, Calibration and Optimization Theory
4)	MRF and Simulated Annealing
5)	Particle Filters/Sequential Important Sampling (SIS)
6)	PF Methods for Tracking
8)	Structure From Motion 1- Early approaches
10)	Multibody motion segmentation
11)	Shape from Shading
12)	Stereo Vision
13)	2D object recognition
14)	Face Recognition

Sources

Course Notes / Textbooks:	Research papers Computer Vision: A Modern Approach (2nd Edition), David A. Forsyth and Jean Ponce, Prentice Hall, 2011.
References:	Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2011.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	3	% 10
Project	1	% 25
Midterms	1	% 25
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 35
PERCENTAGE OF FINAL WORK		% 65
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	4	56
Project	1	15	15
Homework Assignments	3	10	30
Midterms	1	3	3
Final	1	3	3
Total Workload			149

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

Program Outcomes

Level of Contribution