Syllabus Application
Digital Image and Video Analysis
CS 419
Faculty:
Faculty of Engineering and Natural Sciences
Semester:
Fall 2025-2026
Course:
Digital Image and Video Analysis - CS 419
Classroom:
FASS-G062,FENS-L045
Level of course:
Undergraduate
Course Credits:
SU Credit:3.000, ECTS:6, Basic:1, Engineering:5
Prerequisites:
-
Corequisites:
CS 419L
Course Type:
Lecture
Instructor(s) Information
Erchan Aptoula
Course Information
Catalog Course Description
1. Introduction and point processing 2. Binary mathematical morphology 3. Linear image analysis-I 4. Linear image analysis-II 5. Grayscale mathematical morphology 6. Hierarchical image representations 7. Attribute filtering and object based image analysis 8. Color image analysis and color morphology 9. Visual content description 10. Video analysis 11. Motion analysis 12. Image and video compression 13. Spatial and temporal segmentation 14. Recognizing visual patterns
Course Learning Outcomes:
| 1. | Have a clear understanding of how digital images and videos are acquired and stored. |
|---|---|
| 2. | Have a good understanding of the mathematical foundations for the manipulation of digital visual data with both linear and non-linear approaches. |
| 3. | Be able to write programs in Python for preprocessing, processing, analysis, filtering, segmentation, description and compression of digital visual data. |
| 4. | Analyze a wide range of problems and provide solutions related to the design of image and video analysis systems through suitable algorithms, structures, and other methods. |
Course Objective
This course provides a comprehensive introduction into digital image & video processing and analysis.
Sustainable Development Goals (SDGs) Related to This Course:
| Industry, Innovation and Infrastructure |
Major topics include image acquisition, linear and non-linear filtering, color, content description and video analysis. Students will learn the basic concepts of image and video processing as well as acquire hands-on experience in solving real-life visual analysis problems.
Course Materials
Resources:
Textbooks
a) Gonzales and Woods, Digital Image Processing, 4th Ed, 2017
b) W. Pratt, Digital Image Processing, 4th Ed.
c) P. Soille, Morphological Image Analysis, 2004
d) R. Szeliski, Computer Vision and Applications, 2010
a) Gonzales and Woods, Digital Image Processing, 4th Ed, 2017
b) W. Pratt, Digital Image Processing, 4th Ed.
c) P. Soille, Morphological Image Analysis, 2004
d) R. Szeliski, Computer Vision and Applications, 2010