Laboratory assignments for Computer Vision — Applied AI Master’s Programme, Luleå University of Technology. Includes experiments on image processing, segmentation, feature detection, matching, and stereo vision.
Programme: Applied Artificial Intelligence (Master’s)
University: Luleå University of Technology
Course: Computer Vision
Introduces core image processing techniques, including:
- Pixel-level operations
- Image filtering and noise reduction
- Edge detection and thresholding
- Template matching and basic object localization
Explores segmentation and low-level feature analysis through:
- Thresholding and clustering
- Superpixels and region grouping
- Hough transform for line and circle detection
- Object counting and measurement
Focuses on feature extraction and correspondence between images:
- Harris and SIFT detectors
- Feature matching and descriptor comparison
- Geometric verification with RANSAC
- Real-world stereo matching for robotic perception
Covers 3D perception using stereo cameras:
- Camera calibration
- Stereo correspondence and disparity map computation
- Depth estimation from stereo geometry
- Applications to pedestrian detection and distance estimation
Part of the Applied AI Master’s Programme at Luleå University of Technology.