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Camera/Vision for Geo-Location & Geo-Identification

Camera/Vision for Geo-Location & Geo-Identification. John S. Zelek Intelligent Human Machine Interface Lab Dept. of Systems Design Engineering University of Waterloo. Why can’t we use GPS everywhere?. Urban canyons. Indoor navigation. 1. Introduction - 2/20. What we are trying to do.

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Camera/Vision for Geo-Location & Geo-Identification

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  1. Camera/Vision for Geo-Location & Geo-Identification • John S. Zelek • Intelligent Human Machine Interface Lab • Dept. of Systems Design Engineering • University of Waterloo

  2. Why can’t we use GPS everywhere? Urban canyons Indoor navigation 1. Introduction - 2/20

  3. What we are trying to do Accuracy + Location + Maps + Camera Inertial Altimeter, Compass GPS +/- = 1. Introduction – 3/20

  4. Applications 1. Introduction – 4/20

  5. SLAM Given: Dead-reck. Ext. sensor Waypoints Not Known: Map GPS 2. SLAM – 5/20

  6. Trees as landmarks for triangulation 2. SLAM - 6/20

  7. Differentiating different trees Daniel Asmar Slide7 2. SLAM – 7/20

  8. 2. SLAM – 8/20

  9. Object Category Recognition 3. Object Detection & Recognition – 9/20

  10. Classes of Objects vs. Instances 2 instances of an individual object (space shuttle) 2 instances of an object face class 2 instances of an object motorcycle class 3. Object Detection & Recognition – 10/20

  11. Visual vs. Functional classes There is a wide variation in the appearance of objects that are categorized by function. We focus only on categories related by some visual consistency only! 3. Object Detection & Recognition – 11/20

  12. Challenges • changes of viewpoint • transformation (translation, rotation, scaling, affine), out-of-plane (foreshortening) • illumination differences • background clutter • occlusion • intra-class variation 3. Object Detection & Recognition – 12/20

  13. Ours Others Repeatability of our detector appears to be better! 3. Object Detection & Recognition – 13/20

  14. Object Graphs 3. Object Detection & Recognition – 14/20

  15. 3. Object Detection & Recognition – 15/20

  16. 3. Object Detection & Recognition – 16/20

  17. Structure from stereo 4. Structure from Stereo – 17/20

  18. Structure from motion 4. Structure From Motion – 18/20

  19. 5. Context Recognition – 19/20

  20. 6. Closing – 20/20

  21. Extra. Features for Recognition & Structure – 21/20

  22. Extra. Features for Recognition & Structure – 22/20

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