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 Camera Inertial Altimeter, Compass +/- GPS = Accuracy + Location + Maps + 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. Daniel Asmar Slide 7 Differentiating different trees 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. 4. Structure from Stereo – 17/20 Structure from stereo

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