CS414 Computer Vision
CS414 Computer Vision
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Computer Sciences and Engineering
Khaldoun Al Khalidi
Course Lecturer
Course Objectives
The course aims to introduce concepts of Computer Vision and Image Processing. The course covers the image fundamentals and mathematical transforms necessary for image processing, image enhancement techniques, feature extraction from an image and image recognition and/or classification.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Digital Image Processing, Gonzales R.C., 4th Edition, Prentice Hall, 2019
Additional Literature
Teaching Methods
Lectures
Class discussions with examples
Active tutorial sessions for engaged learning and continuous feedback on progress
Homework assignments and Projects
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to Computer Vision systems and its applications and challenges | Chapter 1 |
| 2 | Image Formation. Image Acquisition. Image Sampling and Quantization. Image Representation | Chapter 2 |
| 3 | Image preprocessing (Filtering) | Chapter 3 |
| 4 | Image preprocessing in frequency domain (Filtering) | Chapter 4 |
| 5 | Image Restoration and Reconstruction | Chapter 5 |
| 6 | Image Transforms | Chapter 6 |
| 7 | MIDTERM EXAM | |
| 8 | Color image processing | Chapter 7 |
| 9 | Morphological operations | Chapter 9 |
| 10 | Image segmentation | Chapter 10 |
| 11 | Image segmentation | Chapter 11 |
| 12 | Feature extraction | Chapter 12 |
| 13 | Pattern Recognition and Training | Chapter 13 |
| 14 | Project Presentations | |
| 15 | Project Presentations |
Course Schedule (All Sections)
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes :
Midterm
AI: Not AllowedAlignment with Learning Outcomes :
Assignments
AI: Not AllowedAlignment with Learning Outcomes :
Quiz
AI: Not AllowedAlignment with Learning Outcomes :
Project
AI: Not AllowedAlignment with Learning Outcomes :
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture hours
45 hours ⏳ (15 week × 3 h)
Home study
30 hours ⏳ (15 week × 2 h)
Assignments
30 hours ⏳ (5 week × 6 h)
Midterm exam study
10 hours ⏳ (1 week × 10 h)
Final exam study
10 hours ⏳ (1 week × 10 h)
Final project study
25 hours ⏳ (5 week × 5 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [CS414] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Mar 03, 2026 | International University of Sarajevo
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Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| CS414 | Computer Vision | 3 | 2 | 6 | ||||||
| Prerequisite | MATH201, CS103 | It is a prerequisite to | - | |||||||
| Lecturer | Khaldoun Al Khalidi | Office Hours / Room / Phone | Currently not available |
|||||||
| kalkhalidi@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | sadina@ius.edu.ba | ||||||||
| Course Objectives | The course aims to introduce concepts of Computer Vision and Image Processing. The course covers the image fundamentals and mathematical transforms necessary for image processing, image enhancement techniques, feature extraction from an image and image recognition and/or classification. |
|||||||||
| Textbook | Digital Image Processing, Gonzales R.C., 4th Edition, Prentice Hall, 2019 | |||||||||
| Additional Literature | ||||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Lectures. Class discussions with examples. Active tutorial sessions for engaged learning and continuous feedback on progress. Homework assignments and Projects. | |||||||||
| Teaching Method Delivery | Teaching Method Delivery Notes | |||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to Computer Vision systems and its applications and challenges | Chapter 1 | ||||||||
| Week 2 | Image Formation. Image Acquisition. Image Sampling and Quantization. Image Representation | Chapter 2 | ||||||||
| Week 3 | Image preprocessing (Filtering) | Chapter 3 | ||||||||
| Week 4 | Image preprocessing in frequency domain (Filtering) | Chapter 4 | ||||||||
| Week 5 | Image Restoration and Reconstruction | Chapter 5 | ||||||||
| Week 6 | Image Transforms | Chapter 6 | ||||||||
| Week 7 | MIDTERM EXAM | |||||||||
| Week 8 | Color image processing | Chapter 7 | ||||||||
| Week 9 | Morphological operations | Chapter 9 | ||||||||
| Week 10 | Image segmentation | Chapter 10 | ||||||||
| Week 11 | Image segmentation | Chapter 11 | ||||||||
| Week 12 | Feature extraction | Chapter 12 | ||||||||
| Week 13 | Pattern Recognition and Training | Chapter 13 | ||||||||
| Week 14 | Project Presentations | |||||||||
| Week 15 | Project Presentations | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 30 | Not Allowed | ||
| Semester Evaluation Components | |||||
| Midterm | 1 | 20 | Not Allowed | ||
| Assignments | 5 | 20 | Not Allowed | ||
| Quiz | 1 | 10 | Not Allowed | ||
| Project | 1 | 20 | Not Allowed | ||
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture hours | 3 | 15 | 45 | Home study | 2 | 15 | 30 | |||
| Assignments | 6 | 5 | 30 | Midterm exam study | 10 | 1 | 10 | |||
| Final exam study | 10 | 1 | 10 | Final project study | 5 | 5 | 25 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||