Computational Problem Solving (5 cr)

Assessment criteria

The course is passed by earning a single badge during the competition, or doing one single homework assignment during the course. The number of badges / homework assignments increase the number of credits (max 5 credits).

Evaluation scale

Approved/Rejected

Objective

- Understand and apply mathematical and computational methods for reconstructing movement and drawings from video footage. - Analyze camera perspectives, distortions, and projections to infer spatial relationships. - Design, implement, and evaluate simulations and visual explainers. - Apply optimization techniques such as local search, gradient descent, and neural networks to improve model accuracy. - Implement the solution using a programming language (eg. JavaScript or Python).

Content

The course is structured around a single challenge: reconstructing a pen’s path from footage of its movement in front of colored balls. Through this, students will explore: - Trilateration and geometric localization techniques. - Perspective analysis and size scaling based on visual input. - Camera modeling (pinhole camera, lens distortion). - Map projections (Azimuthal equidistant, Lambert equal-area). - Simulations and visual debugging (using JavaScript and Three.js). - Optimization strategies: local search, gradient descent, and genetic algorithms. - Neural network applications in spatial estimation. - Signal and image processing techniques for segmentation and motion analysis. - Problem decomposition and algorithm design for real-world-inspired scenarios.

Location and time

Online

Materials

Original course notes and explainers by the instructor Szeliski, R. Computer Vision: Algorithms and Applications (optional)

Teaching methods

The course is organized in 2 phases. Phase 1 - The Competition (16.5.2025 - 15.7.2025) Link: https://youtu.be/bZ8uSzZv0ew In this phase, students self-study material they find online, and discuss with the teacher in the forum on Discord (decode-the-drawings channel): https://discord.com/invite/gJFcF5XVn9 Students implement their own solutions and get feedback from the teacher. Depending on their progress, students get badges which translate to credits in this course. Phase 2 - The Course (1.8.2025 - 15.12.2025) The course ask for a number of homework assignments to be completed. Students can choose from a long list, up to 5 assignments to implement (1 assignment = 1 credit point).