Lectures:
1. Image segmentation, edge detection (gradient and zero-crossing methods, Canny detector), key-point detection, Hough transform
2. Variational formulation of image segmentation, level-set approaches, graph approaches, mean-shift approaches
3. Measuring objects for recognition, hand-crafted global object descriptors, universal local descriptors (HOG, SIFT, SURF, LBP)
4. Classification, discriminant functions for classification, support vector machines, neural networks, adaptive boosting (AdaBoost)
5. Deep/convolutional neural networks
6. Reconstructing 3D coordinates from images, camera calibration, finding correspondence between images
7. Analysing images varying in time, object tracking, human action recognition from video sequences
1. Image segmentation, edge detection (gradient and zero-crossing methods, Canny detector), key-point detection, Hough transform
2. Variational formulation of image segmentation, level-set approaches, graph approaches, mean-shift approaches
3. Measuring objects for recognition, hand-crafted global object descriptors, universal local descriptors (HOG, SIFT, SURF, LBP)
4. Classification, discriminant functions for classification, support vector machines, neural networks, adaptive boosting (AdaBoost)
5. Deep/convolutional neural networks
6. Reconstructing 3D coordinates from images, camera calibration, finding correspondence between images
7. Analysing images varying in time, object tracking, human action recognition from video sequences