CS414 Computer Vision
Syllabus | International University of Sarajevo - Last Update on Oct 10, 2026
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Computer Sciences and Engineering
Mohammed Saeed Jawad
Course Lecturer
Course Objectives
The course develops the theoretical knowledge and practical skills needed to analyze images and video and build computer vision applications. Students progress from image representation, geometric transformations and enhancement to segmentation, feature matching, motion analysis, deep learning and real-time object detection. Python and OpenCV laboratory work connects these concepts to working implementations. The course also introduces three-dimensional reconstruction, vision transformers, multimodal AI and explainability, and develops the ability to evaluate accuracy, efficiency, limitations and ethical implications.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
[T1] Richard Szeliski. Computer Vision: Algorithms and Applications. 2nd edition. Springer, 2022. DOI: 10.1007/978-3-030-34372-9 Author website: szeliski.org/Book
Additional Literature
[O1] OpenCV. OpenCV-Python Tutorials. Official documentation for image processing, features, video analysis and camera geometry. OpenCV tutorials [D1] PyTorch. Transfer Learning for Computer Vision Tutorial. Official practical guidance for training and evaluating an image classifier. PyTorch tutorial [Y1] Ultralytics. YOLO Documentation. Official guides for object detection, prediction and model evaluation. YOLO documentation [V1] A. Dosovitskiy et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR, 2021. arXiv 2010.11929 [M1] A. Radford et al. Learning Transferable Visual Models From Natural Language Supervision. ICML, 2021. arXiv 2103.00020 [X1] R. R. Selvaraju et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. ICCV, 2017. arXiv 1610.02391 Weekly references identify selected textbook chapters and supporting resources. The lecturer provides topic-specific notes, datasets and laboratory instructions through the course platform.Teaching Methods
Lectures introduce concepts and algorithms through visual examples and short demonstrations
Class discussions compare methods and analyze errors
Supervised tutorials and laboratory sessions provide guided Python implementation, debugging and continuous feedback
Nine mini-projects develop practical competence, and a final project integrates problem formulation, implementation, evaluation and presentation
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to Computer Vision, Course Overview, AI vs Image Processing vs Computer Vision, Applications, Python & OpenCV Setup, Image Representation | T1 Chs. 1–2; O1 |
| 2 | Digital Images, Pixels, Resolution, Color Models (RGB, HSV, LAB, Grayscale), Image Histograms | T1 Chs. 2–3; O1 |
| 3 📍 | Geometric Transformations: Scaling, Translation, Rotation, Reflection, Affine & Perspective Transformations | T1 Chs. 2–3; O1 |
| 4 | Image Enhancement: Noise Models, Smoothing, Gaussian Filter, Median Filter, Bilateral Filter, Sharpening | T1 Ch. 3; O1 |
| 5 | Binary Images, Thresholding, Morphological Operations (Erosion, Dilation, Opening, Closing), Connected Components | T1 Ch. 3; O1 |
| 6 | Image Segmentation, Edge Detection (Sobel, Prewitt, Laplacian, Canny), Watershed Segmentation | T1 Chs. 3, 6; O1 |
| 7 | Feature Detection: Harris Corner Detector, SIFT, ORB, Feature Descriptors, Feature Matching | T1 Ch. 7; O1 |
| 8 | Midterm Examination + Panorama Stitching (Homography, RANSAC) | T1 Chs. 4, 8; O1 |
| 9 | Shape Detection using Hough Transform, Contour Detection, Object Measurement | T1 Chs. 4, 7; O1 |
| 10 | Motion Detection, Background Subtraction, Optical Flow, Object Tracking Fundamentals | T1 Ch. 9; O1 |
| 11 | Deep Learning for Computer Vision: CNN Fundamentals, Image Classification, Transfer Learning | T1 Chs. 5–6; D1 |
| 12 | Modern Object Detection: YOLO Architecture, Real-Time Detection, Evaluation Metrics (IoU, Precision, Recall, mAP) | T1 Chs. 5–6; Y1 |
| 13 | Advanced Applications: Face Detection, OCR, Medical Imaging, Autonomous Vehicles, Smart CCTV, 3D Reconstruction | T1 Chs. 6, 11–13; O1 |
| 14 | Vision Transformers (ViTs), Multimodal AI, Explainable Computer Vision, Ethics, Project Presentations and Course Review | T1 Chs. 5–6; V1, M1, X1 |
| 15 | Final Review before Final |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| CS414.1 | Course | Tuesday 15:00 - 17:50 | B F2.6 | - | - |
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Computer Vision Laboratory Mini-projects
AI: Consult InstructorAlignment with Learning Outcomes : 1 2 3 4
Attendance and Participation
AI: Not AllowedAlignment with Learning Outcomes : 3 4
Final Project
AI: Consult InstructorAlignment with Learning Outcomes : 1 2 3 4
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)
Mini-project Labs
18 hours ⏳ (9 week × 2 h)
Practice and supervised laboratory hours
22 hours ⏳ (11 week × 2 h)
Final examination preparation and examination
10 hours ⏳ (1 week × 10 h)
Final project independent work
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 Oct 10, 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 | Mohammed Saeed Jawad | Office Hours / Room / Phone | Currently not available |
|||||||
| msjawad@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | sadina@ius.edu.ba | ||||||||
| Course Objectives | The course develops the theoretical knowledge and practical skills needed to analyze images and video and build computer vision applications. Students progress from image representation, geometric transformations and enhancement to segmentation, feature matching, motion analysis, deep learning and real-time object detection. Python and OpenCV laboratory work connects these concepts to working implementations. The course also introduces three-dimensional reconstruction, vision transformers, multimodal AI and explainability, and develops the ability to evaluate accuracy, efficiency, limitations and ethical implications. | |||||||||
| Textbook | [T1] Richard Szeliski. Computer Vision: Algorithms and Applications. 2nd edition. Springer, 2022. DOI: 10.1007/978-3-030-34372-9 Author website: szeliski.org/Book | |||||||||
| Additional Literature |
|
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| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
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| Teaching Methods | Lectures introduce concepts and algorithms through visual examples and short demonstrations. Class discussions compare methods and analyze errors. Supervised tutorials and laboratory sessions provide guided Python implementation, debugging and continuous feedback. Nine mini-projects develop practical competence, and a final project integrates problem formulation, implementation, evaluation and presentation. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to Computer Vision, Course Overview, AI vs Image Processing vs Computer Vision, Applications, Python & OpenCV Setup, Image Representation | T1 Chs. 1–2; O1 | ||||||||
| Week 2 | Digital Images, Pixels, Resolution, Color Models (RGB, HSV, LAB, Grayscale), Image Histograms | T1 Chs. 2–3; O1 | ||||||||
| Week 3 | Geometric Transformations: Scaling, Translation, Rotation, Reflection, Affine & Perspective Transformations | T1 Chs. 2–3; O1 | ||||||||
| Week 4 | Image Enhancement: Noise Models, Smoothing, Gaussian Filter, Median Filter, Bilateral Filter, Sharpening | T1 Ch. 3; O1 | ||||||||
| Week 5 | Binary Images, Thresholding, Morphological Operations (Erosion, Dilation, Opening, Closing), Connected Components | T1 Ch. 3; O1 | ||||||||
| Week 6 | Image Segmentation, Edge Detection (Sobel, Prewitt, Laplacian, Canny), Watershed Segmentation | T1 Chs. 3, 6; O1 | ||||||||
| Week 7 | Feature Detection: Harris Corner Detector, SIFT, ORB, Feature Descriptors, Feature Matching | T1 Ch. 7; O1 | ||||||||
| Week 8 | Midterm Examination + Panorama Stitching (Homography, RANSAC) | T1 Chs. 4, 8; O1 | ||||||||
| Week 9 | Shape Detection using Hough Transform, Contour Detection, Object Measurement | T1 Chs. 4, 7; O1 | ||||||||
| Week 10 | Motion Detection, Background Subtraction, Optical Flow, Object Tracking Fundamentals | T1 Ch. 9; O1 | ||||||||
| Week 11 | Deep Learning for Computer Vision: CNN Fundamentals, Image Classification, Transfer Learning | T1 Chs. 5–6; D1 | ||||||||
| Week 12 | Modern Object Detection: YOLO Architecture, Real-Time Detection, Evaluation Metrics (IoU, Precision, Recall, mAP) | T1 Chs. 5–6; Y1 | ||||||||
| Week 13 | Advanced Applications: Face Detection, OCR, Medical Imaging, Autonomous Vehicles, Smart CCTV, 3D Reconstruction | T1 Chs. 6, 11–13; O1 | ||||||||
| Week 14 | Vision Transformers (ViTs), Multimodal AI, Explainable Computer Vision, Ethics, Project Presentations and Course Review | T1 Chs. 5–6; V1, M1, X1 | ||||||||
| Week 15 | Final Review before Final | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 30 | 1,2,3,4 | Not Allowed | |
| Semester Evaluation Components | |||||
| Computer Vision Laboratory Mini-projects | 9 | 45 | 1,2,3,4 | Consult Instructor | |
| Attendance and Participation | 1 | 5 | 3,4 | Not Allowed | |
| Final Project | 1 | 20 | 1,2,3,4 | Consult Instructor | |
| *** 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 | |||
| Mini-project Labs | 2 | 9 | 18 | Practice and supervised laboratory hours | 2 | 11 | 22 | |||
| Final examination preparation and examination | 10 | 1 | 10 | Final project independent work | 5 | 5 | 25 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 04/10/2026 | |||||||||