Lectures:
1. Detecting edges in images, gradient methods, zero-crossing method, parametric edge models.
2. Image segmentation by region growing/splitting, thresholding, optimal threshold selection, adaptive thresholding.
3. Canny edge detector, Hough transform.
4. Detecting the feature points in images.
5. Measuring the objects for recognition, selecting and computing the descriptors.
6. Evaluating the efficiency of descriptors, introduction to universal descriptors (HoG).
7. Classification using discriminant functions, clustering, and SVM.
8. Classification using classical shallow neural networks.
9. Introduction to the deep neural networks, network architectures for recognising objects in images.
10. Creating 3D models from images, camera calibration, 3D sensors.
11. The problem of finding the correspondence between the images, and some methods for its solution.
12. Analysis of 3D point clouds, detecting the feature points, computing the descriptors, geometric consistency.
13. Analysis of images changing in time, optical flow, object tracking, Kalman filtering.
14. Introduction to action recognition from video frames.
Computer labs:
1. Edge detection, gradient and zero-crossing methods.
2. Canny edge detector, parametric edge models.
3. Thresholding, optimal threshold selection.
4. Hough transform.
5. Selecting the features/descriptors for classification.
6. Optimizing the set of descriptors, universal descriptors (HoG).
7. Classification using etalons and discriminant functions.
8. Classification using k-means clustering, classification using SVM.
9. Classification using shallow neural networks.
10. Classification using deep neural networks.
11. Classification using deep neural networks – continuation.
12. Optical flow.
13. Tracking the objects in video frames.
14. Obtaining the credit.
1. Detecting edges in images, gradient methods, zero-crossing method, parametric edge models.
2. Image segmentation by region growing/splitting, thresholding, optimal threshold selection, adaptive thresholding.
3. Canny edge detector, Hough transform.
4. Detecting the feature points in images.
5. Measuring the objects for recognition, selecting and computing the descriptors.
6. Evaluating the efficiency of descriptors, introduction to universal descriptors (HoG).
7. Classification using discriminant functions, clustering, and SVM.
8. Classification using classical shallow neural networks.
9. Introduction to the deep neural networks, network architectures for recognising objects in images.
10. Creating 3D models from images, camera calibration, 3D sensors.
11. The problem of finding the correspondence between the images, and some methods for its solution.
12. Analysis of 3D point clouds, detecting the feature points, computing the descriptors, geometric consistency.
13. Analysis of images changing in time, optical flow, object tracking, Kalman filtering.
14. Introduction to action recognition from video frames.
Computer labs:
1. Edge detection, gradient and zero-crossing methods.
2. Canny edge detector, parametric edge models.
3. Thresholding, optimal threshold selection.
4. Hough transform.
5. Selecting the features/descriptors for classification.
6. Optimizing the set of descriptors, universal descriptors (HoG).
7. Classification using etalons and discriminant functions.
8. Classification using k-means clustering, classification using SVM.
9. Classification using shallow neural networks.
10. Classification using deep neural networks.
11. Classification using deep neural networks – continuation.
12. Optical flow.
13. Tracking the objects in video frames.
14. Obtaining the credit.