BIG DATA ANALYTICS AND MANAGEMENT (ENGLISH, NONTHESIS)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
CMP5550	Computer Vision	Fall Spring	3	0	3	8

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 :	Assist. Prof. ERKUT ARICAN
Course Lecturer(s):	Assist. Prof. TARKAN AYDIN
Recommended Optional Program Components:	None
Course Objectives:	This class introduces the fundamental techniques in computer vision. Initially basic concepts of image formation, representation and camera projection geometries will be given. Later some classical image processing techniques will be introduced such as edge detection, segmentation, thresholding etc. Image matching, optical flow, local image features will be described in the context of multiple image processing. Basic image recognition techniques are also to be introduced. 3D inference will be another focus where stereo imaging, 3D reconstruction and various shape from X techniques are to be discussed.

Learning Outcomes

The students who have succeeded in this course;
Upon successful completion of this course, students will be able to:
1- List the main components of the computer vision processes
2- Identify the latest development of Computer Vision
3- Apply 3D vision techniques in real world applications

Course Content

This course offers a comprehensive introduction to the fundamental techniques and applications of computer vision. Students will begin by exploring digital image processing, edge and feature detection, and image transformations. The course then delves into camera models, calibration, and 3D scene reconstruction using stereo vision and structure from motion. Practical topics such as motion segmentation, object recognition, and image warping are covered through hands-on programming exercises and real-world datasets. The course concludes with student-led term projects that demonstrate the integration of these techniques into complete vision systems.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Fundamental Concepts
2)	Digital Image processing techniques
3)	Edge Detection
4)	Line and curve detection
5)	Camera Calibration
6)	Stereo Vision
7)	Image segmentation
8)	Optical flow
9)	Analysis of visual Motion
10)	Shape from focus-defocus
11)	Shape From Motion
12)	Shape From Motion
13)	Object detection and Recognition
14)	Object detection and Recognition

Sources

Course Notes / Textbooks:	"Computer Vision: Algorithms and Applications", Richard Szeliski "Introductory Techniques for 3-D Computer Vision", Trucco and Verri "Computer vision: A Modern Approach," David A. Forsyth, Jean Ponce • “Machine Vision” by Ramesh Jain, Rangachar Kasturi, Brian G. Schunck
References:	Ceemple OpenCV IDE - https://www.ceemple.com/

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	3	% 30
Project	1	% 20
Midterms	1	% 20
Final	1	% 30
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	2	28
Project	1	30	30
Homework Assignments	4	12	48
Midterms	1	20	20
Final	1	30	30
Total Workload			198

Contribution of Learning Outcomes to Programme Outcomes

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

	Program Outcomes	Level of Contribution
1)	To be able to follow and critically analyze scientific literature and use it effectively in solving engineering problems.
2)	To be able to design, plan, implement and manage original projects related to Big Data Analytics and Management.
3)	To be able to carry out studies on Big Data Analytics and Management independently, take scientific responsibility and critically evaluate the results obtained.
4)	Effectively present the results of his/her research and projects in written, oral and visual form in accordance with academic standards.
5)	To be able to conduct independent research in the field of Big Data Analytics and Management, develop original ideas and transfer this knowledge to practice.
6)	Uses advanced theoretical and practical knowledge specific to the field of Big Data Analytics and Management effectively.
7)	Acts in accordance with professional, scientific and ethical values; takes responsibility by considering the social, environmental and ethical impacts of engineering applications.