Learn the theory and algorithms of video signal analysis, and finding features and objects in image/video. Learn the basics of 3D image processing, sensing in 3D and 3D model reconstruction. Learn the practical basics of MATLAB programming and analysis and/or 3D algorithms for practical applications in surveillance and medical imaging and learn the basics of deep learning.
Content of the course is divided globally into three areas.
- New transformation techniques (wavelets and projective) for analysis in medical domain and 3D modeling in surveillance. The application of wavelets is given using the JPEG-2000 standard.
- 3D processing based on the camera pinhole model, homography and multi-view processing and calibration. Also registration of 3D datasets, 3D reconstruction models with TSDF, introduction to SLAM, RGB-Depth processing and specific algorithms like G2.0 and bundle adjustment. Finally, the 3D processing modules end with plane/object segmentation in 3D.
- Techniques for object detection and recognition, feature extraction and analysis, like SIFT and Laplacian methods. Furthermore, semantic level processing for understanding events and scenes, including human behavior. Furthermore, classification techniques for understanding objects and events. Modern classification like K-means and SVM (support vector machine) algorithms, evolving into basics of learning with neural nets. This part will gradually evolve to deep learning fundamentals.
The computer assignments aim at applying the knowledge and algorithms (or parts of them) to provide the student a framework for experiments with video content understanding and 3D image-babsed modeling for both surveillance and medical applications.
5LSE0 - Multimedia Video Coding and Architectures (recommended)
5XSA0 - Introduction Medical Imaging Processing (recommended)
|28 aug||09.30-12.30||Flux 1.11||Module 1A+1B: Wavelet transformation theory + JPEG-2000||Prof. P.H.N. de With|
|28 aug||13.30-14.30||Flux 1.11||Module 1B: JPEG-2000||Prof. P.H.N. de With|
|28 aug||14.30-17.30||Flux 1.11||Module 2: 3D Pinhole camera, projective transformation||Dr. E. Bondarev|
|29 aug||09.30-12.30||Flux 1.11||Module 3: 3D Registration, data fusion and SLAM reconstruction||Dr. E. Bondarev|
|29 aug||13.30-17.30||Flux 1.11||Module 4: Visual feature extraction||Dr. S. Zinger|
|30 aug||09.30-12.30||Flux 1.11||Module 5: Motion analysis and estimation||Prof. P.H.N. de With|
|30 aug||13.30-17.30||Flux 1.11||Module 6: Object-level content analysis - segmentation||A. Pourtaherian MSc.|
|31 aug||09.30-12.30||Flux 1.11||Module 7: Object-level content analysis - tracking||A. Pourtaherian MSc.|
|31 aug||13.30-17.30||Flux 1.11||Module 8: Semantic-level content analysis and clustering + classification||F. van der Sommen Msc.|
|1 sept||09.30-12.30||Flux 1.11||Module 9A: Deep learning introduction||F. van der Sommen Msc.|
|1 sept||13.30-16.30||Flux 1.11||Module 9B: Deep learning work out||F. Ghazvinian Zanjani|
|1 sept||16.30-17.30||Flux 1.11||Module 10: Case study application||Prof. P.H.N. de With|