COURSE UNIT TITLE

: COMPUTER VISION

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EEE 5034 COMPUTER VISION ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

Offered to

ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)
ELECTRICAL AND ELECTRONICS ENGINEERING NON -THESIS (EVENING PROGRAM) (ENGLISH)
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)
Artificial Intelligence and Intelligent Systems
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)

Course Objective

The aim of this course is to give students a firm understanding of the theory underlying the processing and interpretation of visual information and the ability to apply that understanding to ubiquitous computing and entertainment related problems. It provides them with an opportunity to apply their problem-solving skills to an area which, while it is firmly part of computer science/engineering, draws strongly from other disciplines (physics, optics, psychology). The course is based around problems so that the technology is always presented in context. During the course, students design solutions to real world problems using the techniques that they have been taught.

Learning Outcomes of the Course Unit

1   To be able to design solutions to real-world problems using computer vision.
2   To be able develop working computer vision systems using MATLAB.
3   To be able to critically appraise computer vision techniques.
4   To be able to explain, compare and contrast computer vision techniques.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction
2 Image formation
3 Image features
4 Features (continued)
5 Segmentation
6 Image stitching
7 Computational photography
8 Optical flow
9 Recognition 1: general concepts and eigenfaces
10 Recognition 2: feature-based instance and category recognition
11 Midterm exam
12 Structure from motion
13 Stereo
14 Matting and Transparency

Recomended or Required Reading

Computer Vision Algorithms and Applications, Richard Szeliski, Springer, 2010
Computer Vision: A Modern Approach, Forsyth and Ponce, Pearson, 2003

Planned Learning Activities and Teaching Methods

Lecture+Homeworks+Project +Exam

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 PRJ PROJECT
3 FINAP PROJECT
4 FCG FINAL COURSE GRADE ASG * 0.20 + PRJ *0.35 + FINAP * 0.45


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

metehan.makinaci @ deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 13 3 39
Preparation for midterm exam 1 15 15
Preparing assignments 4 15 60
Design Project 1 40 40
Preparing presentations 1 5 5
Midterm 1 2 2
TOTAL WORKLOAD (hours) 200

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.14445553
LO.23215453
LO.34112412
LO.42112

CONTACT INFORMATION
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Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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