• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. List the main components of the computer vision processes

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Computer Engineering
   2. Formulate and solve advanced engineering problems
   3. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
   4. Work effectively in multi-disciplinary research teams
   5. Acquire scientific knowledge
   6. Continuously develop their knowledge and skills in order to adapt to a rapidly developing technological environment,

   Method of assessment

   1. Written exam
2. Identify the latest development of Computer Vision

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Computer Engineering
   2. Formulate and solve advanced engineering problems
   3. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
   4. Work effectively in multi-disciplinary research teams
   5. Acquire scientific knowledge
   6. Design and conduct research projects independently
   7. Continuously develop their knowledge and skills in order to adapt to a rapidly developing technological environment,
   8. Find out new methods to improve his/her knowledge.

   Method of assessment

   1. Written exam
   2. Homework assignment
3. Apply 3D vision techniques in real world applications

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Computer Engineering
   2. Formulate and solve advanced engineering problems
   3. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
   4. Work effectively in multi-disciplinary research teams
   5. Acquire scientific knowledge
   6. Continuously develop their knowledge and skills in order to adapt to a rapidly developing technological environment,
   7. Find out new methods to improve his/her knowledge.

   Method of assessment

   1. Homework assignment

Contents
Week 1:	Fundamental Concepts
Week 2:	Fundamental Concepts
Week 3:	Digital Images
Week 4:	Digital Images
Week 5:	Perspective and Orthographic Cameras
Week 6:	Perspective and Orthographic Cameras
Week 7:	Midterm exam
Week 8:	Camera Calibration
Week 9:	Camera Calibration
Week 10:	Stereo Vision
Week 11:	Stereo Vision
Week 12:	Analysis of Visual Motion
Week 13:	Analysis of Visual Motion
Week 14:	Object Modeling and Recognition
Week 15*:	Object Modeling and Recognition
Week 16*:	Final exam
Textbooks and materials:	• Introductory Techniques for 3-D Computer Vision, Trucco and Verri
Recommended readings:	• “Computer vision: A Modern Approach,” David A. Forsyth, Jean Ponce • “Machine Vision” by Ramesh Jain, Rangachar Kasturi, Brian G. Schunck
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	7	30
Other in-term studies:		0
Project:		0
Homework:	2-12	30
Quiz:		0
Final exam:	16	40
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	3	14
Practice, Recitation:	0	0
Homework:	7	10
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	10	1
Mid-term:	1	1
Personal studies for final exam:	20	1
Final exam:	2	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)