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2 Computer Vision Labs

2 Computer Vision Labs. Prof. Luc Van Gool ETH - Switzerland Un. Leuven – Belgium appr. 15 researchers appr. 15 researchers Tracking Recognition Recognition Passive 3D Active 3D Hum.-comp. interact.

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2 Computer Vision Labs

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  1. 2 Computer Vision Labs Prof. Luc Van Gool ETH - SwitzerlandUn. Leuven – Belgium appr. 15 researchers appr. 15 researchers Tracking Recognition Recognition Passive 3D Active 3D Hum.-comp. interact.

  2. An overview of some vision trends • scene reconstruction • recognition • tracking

  3. Scene reconstruction • 3D acquisition with off-the-shelf HW • 4D capture: dynamic 3D • Realistic texture synthesis • City modelling • Intuitive visualisation

  4. 3D acquisition One-shot ShapeCam

  5. 3D acquisition

  6. 3D from hand-held camera images

  7. ... The result generated by ARC3D

  8. 4D acquisition 3D snapshots in fast succession

  9. Shape-from-silhouettes

  10. Outdoor visual hulls

  11. Realistic texturing Stochastic & hierarchical texture models Viewpoint/illumination dependent texture Minidome: portable photometric stereo

  12. Realistic texture Given examples Synthetic textures IKT

  13. AUTOMATIC Realistic texture

  14. Recognition

  15. Object recognition • Independent of viewpoint • Irrespective of occlusion • In the presence of scene clutter • Under variable illumination • Robust against deformations Latest techniques based on `invariant regions’

  16. The ellipses show invariant regions, they cover the same part of the scene Object recognition The crux is that they were found independently

  17. Searching for the van in `groundhog day’ Object recognition • Example application: automatic annotation of video data • E.g. finding same object somewhere else in a complete movie

  18. Automatic retrievalof all scenes with the van based onthe example image

  19. Object recognition • Next challenge: categorisation • i.e. not recognising particular objects, but rather the class an object belongs to, e.g. a car, a person, etc. This is more difficult, because of the Intra-class variability…

  20. Object recognition • Next challenge: categorisation

  21. Finding people • Security / surveillance / annotation e.g. pedestrian detector

  22. Tracking • Robust blob tracking – anti-drift • Body pose tracking • Detailed hand tracking • Action recognition • Gait analysis

  23. Multi-feature tracker

  24. HandyMouse project Skin color Detection, Tracking, and Gesture analysis For Minority Report style interaction

  25. Marker-less motion capture

  26. Spin-offs / start-ups ICOS Eyetronics GeoAutomation eSaturnus Kooaba Procedural

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