COURSE INFORMATION Course Title: COMPUTER VISION

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
ECE 468	B	1	3	2	0	4	7.5

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	Dr. Florenc Skuka fskuka@epoka.edu.al
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Dr. Florenc Skuka fskuka@epoka.edu.al
Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Compulsory
Study program: (the study for which this course is offered)	Master of Science in Electronics and Communication Engineering
Classroom and Meeting Time:
Teaching Assistant(s) and Office Hours:	NA
Code of Ethics:	Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline"
Attendance Requirement:
Course Description:	Introduction to computer vision. Image matting. Optical flow. Motion Capture. Filters. Segmentation. Fourier Theory.
Course Objectives:	The course emphasizes research topics that underlie the advanced visual effects that are becoming increasingly common in commercials, music videos and movies. Topics include classical computer vision algorithms and exciting recent developments that form the basis for future effects (such as natural image matting, multi-image compositing, image retargeting, and view synthesis). We also discuss the technologies behind motion capture and three-dimensional data acquisition. Analysis of behind-the-scenes videos and in-depth interviews with visual effects tie the mathematical concepts to real-world filmmaking.

BASIC CONCEPTS OF THE COURSE

1

COURSE OUTLINE

Week	Topics
1	Overview of Computer Vision and Visual Effects.
2	Image Formation
3	Image Processing
4	Image Filtering
5	Feature Detectors and Descriptor.
6	Feature evaluation and use.
7	Parametric Transformations and Scattered Data Interpolation.
8	Image formation and single-camera calibration.
9	Midterm Exam
10	Optical flow and Epipolar geometry.
11	3D Reconstruction
12	Segmentation
13	Deep Learning for Computer Vision
14	Project Presentation

Prerequisite(s):	Signals & Systems, Digital Signal Processing and Image Processing.
Textbook(s):	1 - Computer Vision: Algorithms and Applications ,Rick Szeliski 2 -Computer Vision for Visual Effects by Richard J. Radke
Additional Literature:
Laboratory Work:
Computer Usage:
Others:	No

COURSE LEARNING OUTCOMES

1	Learn image representation and analyze binary images.
2	Learn to filter – enhance images and pattern recognition from images.
3	Understand graph cut segmentation, video matting, and matting extensions.
4	Explain multiresolution blending and Poisson image editing.
5	Understand photomontage and image Inpainting.
6	Explain feature detectors.
7	Explain video matching, morphing, and view synthesis.
8	Knows how to make motion capture setup and forward kinematics and Inverse kinematics and motion editing.
9	Understand facial and markerless motion capture, LiDAR and time-of-flight sensing, Structured light scanning.

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Master of Science in Electronics and Communication Engineering Program
1	Engineering graduates with sufficient theoretical and practical background for a successful profession and with application skills of fundamental scientific knowledge in the engineering practice.	5
2	Engineering graduates with skills and professional background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations	5
3	Engineering graduates with the necessary technical, academic and practical knowledge and application confidence in the design and assessment of machines or mechanical systems or industrial processes with considerations of productivity, feasibility and environmental and social aspects.	4
4	Engineering graduates with the practice of selecting and using appropriate technical and engineering tools in engineering problems, and ability of effective usage of information science technologies.	5
5	Ability of designing and conducting experiments, conduction data acquisition and analysis and making conclusions.	5
6	Ability of identifying the potential resources for information or knowledge regarding a given engineering issue.	4
7	The abilities and performance to participate multi-disciplinary groups together with the effective oral and official communication skills and personal confidence.	5
8	Ability for effective oral and official communication skills in foreign language.	4
9	Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology.	5
10	Engineering graduates with well-structured responsibilities in profession and ethics.	5
11	Engineering graduates who are aware of the importance of safety and healthiness in the project management, workshop environment as well as related legal issues.	5
12	Consciousness for the results and effects of engineering solutions on the society and universe, awareness for the developmental considerations with contemporary problems of humanity.	5

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Homework	6	5
Project	1	30
Final Exam	1	40
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	5	80
Hours for off-the-classroom study (Pre-study, practice)	10	4	40
Mid-terms	1	20	20
Assignments			0
Final examination	1	27.5	27.5
Other	1	20	20
Total Work Load:			187.5
Total Work Load/25(h):			7.5
ECTS Credit of the Course:			7.5

CONCLUDING REMARKS BY THE COURSE LECTURER