CE 466 | Course Introduction and Application Information - Department of Software Engineering

Course Name

Computer Vision

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 466	Fall/Spring	3	0	3	5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	First Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	Application: Experiment / Laboratory / Workshop Lecture / Presentation
National Occupation Classification	-
Course Coordinator	Dr. Öğr. Üyesi Kaya OĞUZ
Course Lecturer(s)	-
Assistant(s)	-

Course Objectives

This course is designed to introduce fundamental principles and applications of computer vision. During the course, the fundamental concepts of computer vision will be discussed, real-world applications of computer vision will be described, and students will participate in a project where they will apply computer vision algorithms.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	will be able to describe theoretical and practical aspects of signal processing using images,
2	will be able to explain the principles of image formation and analysis,
3	will be able to discuss main technical approaches in computer vision,
4	will be able to express basics of measurement and robust detection of features in images,
5	will be able to describe various methods used for registration, alignment, and matching of images.

Course Description

The following topics will be included: image formation, image processing, feature detection and matching, segmentation, feature-based alignment, structure from motion, dense motion estimation, image stitching, computational photography, stereo correspondence, 3D reconstructions, image-based rendering, and recognition.

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation	Learning Outcome
1	Introduction to Computer Vision	Chapter 1. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
2	Image Formation	Chapter 2. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
3	Image Processing	Chapter 3. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
4	Feature Detection and Matching	Chapter 4. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
5	Segmentation	Chapter 5. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
6	Feature-Based Alignment	Chapter 6. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
7	Structure From Motion	Chapter 7. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
8	Dense Motion Estimation	Chapter 8. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
9	Midterm exam
10	Image Stitching	Chapter 9. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
11	Computational Photography	Chapter 10. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
12	Stereo Correspondence	Chapter 11. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
13	3D Reconstruction	Chapter 12. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
14	Project Presentations
15	Semester Review
16	Final Exam

Course Notes/Textbooks

Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.

Suggested Readings/Materials

Shapiro and Stockman, Computer Vision, Prentice-Hall, 2001; Deep Learning, by Goodfellow, Bengio, and Courville; Dictionary of Computer Vision and Image Processing, by Fisher et al.

Deep Learning, by Goodfellow, Bengio, and Courville. ISBN: 978-0262035613;

Dictionary of Computer Vision and Image Processing, by Fisher et al. ISBN: 978-1119941866

Semester Activities	Number	Weigthing	LO 1	LO 2	LO 3	LO 4	LO 5
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project	1	30
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	2	60
Weighting of End-of-Semester Activities on the Final Grade	1	40
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	14	2	28
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury			0
Project	1	30	30
Seminar / Workshop			0
Oral Exam			0
Midterms	1	20	20
Final Exam	1	24	24
		Total	150

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computer computation, and topics specific to related engineering disciplines; the ability to use this knowledge in solving complex engineering problems		-	X	-	-	-
	1	Mathematics	-	-	-	-	-
	2	Science	-	-	-	-	-
	3	Basic engineering	-	-	-	-	-
	4	Computer computation	-	-	-	-	-
	5	Topics specific to related engineering disciplines	-	-	-	-	-
	6	The ability to use this knowledge in solving complex engineering problems	-	-	-	-	-
2	Problem Analysis: The ability to define, formulate, and analyze complex engineering problems by using fundamental science, mathematics, and engineering knowledge, while considering the relevant UN Sustainable Development Goals (SDGs) related to the problem.		-	-	-	-	X
3	Engineering Design: The ability to design creative solutions to complex engineering problems; the ability to design complex systems, processes, devices, or products that meet present and future requirements, considering realistic constraints and conditions.		-	-	-	-	-
	1	The ability to design creative solutions to complex engineering problems	-	-	-	-	-
	2	Considering realistic constraints and conditions in designing complex systems, processes, devices, or products	-	-	-	-	-
	3	The ability to design in a way that meets current and future requirements	-	-	-	-	-
4	Use of Techniques and Tools: The ability to select and use appropriate techniques, resources, and modern engineering and information technology tools, including prediction and modeling, for the analysis and solution of complex engineering problems, while being aware of their limitations		-	-	-	X	-
5	Research and Investigation: The ability to use research methods, including literature review, designing experiments, conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems.		-	-	-	-	-
	1	The ability to use research methods, including literature review	-	-	-	-	-
	2	Designing experiments	-	-	-	-	-
	3	Conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems	-	-	-	-	-
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs); awareness of the legal consequences of engineering solutions		-	-	-	-	-
	1	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs)	-	-	-	-	-
	2	Awareness of the legal consequences of engineering solutions	-	-	-	-	-
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility; awareness of acting impartially and inclusively, without discrimination in any matter. (FENG101)		-	-	-	-	-
	1	Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility	-	-	-	-	-
	2	Awareness of acting impartially and inclusively, without discrimination in any matter.	-	-	-	-	-
8	Individual and Team Work: The ability to work effectively as an individual and as a member or leader of both intra-disciplinary and interdisciplinary teams (whether face-to-face, remote, or hybrid).		-	-	-	-	-
9	Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession), particularly in technical matters.		-	-	-	-	-
	1	Verbal (ENGxxx)	-	-	-	-	-
	2	Written effective communication skills. (ENGxxx)	-	-	-	-	-
10	Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.		-	-	-	-	-
	1	Knowledge of business practices such as project management and economic feasibility analysis; (FENG497-FENG498)	-	-	-	-	-
	2	Awareness of entrepreneurship and innovation. (FENG101)	-	-	-	-	-
11	Lifelong Learning: The ability to learn independently and continuously, adapt to new and emerging technologies, and think critically about technological changes.		-	-	-	-	-

CE 466 | Course Introduction and Application Information

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