A. Opelt, M. Fussenegger, A. Pinz, P. Auer

1. A. Opelt, M. Fussenegger, A. Pinz, P. Auer Weak Hypotheses and Boosting for GenericObject Detection and Recognition Andreas Opelt (Graz University of Technology and University of Leoben)

2. Agenda • The Basic Idea • Our Framework for generic Object Recognition  • The techniques used • The Learning Model  • Our Model • The Weak Hypotheses Finder • Experiments • Discussion / Outlook Andreas Opelt (Graz University of Technology and University of Leoben)

3. The Basic Idea 1/2 Problems ? Object is located anywhere in the image! No pre-selection of the object ! Any instance of the object category! Not special images for learning! We want to go towards ‘real’ Generic Object Recognition! Arbitrary view of the object! Any background clutter! Objects shown in any arbitrary scale! Not only for a special category of objects! Andreas Opelt (Graz University of Technology and University of Leoben)

4. The Basic Idea 2/2 We want to go towards ‘real’ Generic Object Recognition! We want to go towards ‘real’ Generic Object Recognition! Graz database; Bikes, Persons, Background Oxford database; (Fergus, Perona and Zisserman, CVPR 2003) Agarwal and Roth, ECCV 2002, Cars side database Andreas Opelt (Graz University of Technology and University of Leoben)

5. The Framework Andreas Opelt (Graz University of Technology and University of Leoben)

6. Region Extraction 1/2 [Mikolajczyk/Schmid 2001] Data reduction: Threshold [Mikolajczyk/Schmid 2001] Data reduction: Threshold [Lowe 1999] (Diff. of Gaussian) Data reduction: Clustering Andreas Opelt (Graz University of Technology and University of Leoben)

7. Region Extraction 2/2 Andreas Opelt (Graz University of Technology and University of Leoben)

8. Region Normalization • Homomorphic Filtering [Gonzales and Woods, C. 4.5.] • Size Normalization Andreas Opelt (Graz University of Technology and University of Leoben)

9. The Framework Andreas Opelt (Graz University of Technology and University of Leoben)

10. Local Descriptors Subsampled Grayvalues Basic Moments (Dim=10) [L. Van Gool 1996] Dim=9 [D. Lowe 1999] Dim=128 (3 orient. planes, 8x8px) Andreas Opelt (Graz University of Technology and University of Leoben)

11. The Framework Andreas Opelt (Graz University of Technology and University of Leoben)

12. The Learning Model 1/3 Input: Weak Hypotheses:  Threshold, Weight Output: Andreas Opelt (Graz University of Technology and University of Leoben)

13. The Learning Model 1/3 Select best Weak Hypothesis Calculate Threshold Andreas Opelt (Graz University of Technology and University of Leoben)

14. The Learning Model 3/3 Andreas Opelt (Graz University of Technology and University of Leoben)

15. Experiments 1/6 Category: Bikes  some Weak Hypotheses Andreas Opelt (Graz University of Technology and University of Leoben)

16. Experiments 2/6  Testing BIKE ! Andreas Opelt (Graz University of Technology and University of Leoben)

17. Experiments 3/6  Testing BIKE ! BIKE ! Andreas Opelt (Graz University of Technology and University of Leoben)

18. Experiments 4/6  Testing NO BIKE ! NO BIKE ! Andreas Opelt (Graz University of Technology and University of Leoben)

19. Experiments 5/6  Testing NO BIKE ! Andreas Opelt (Graz University of Technology and University of Leoben)

20. Experiments 6/6 Facts: Andreas Opelt (Graz University of Technology and University of Leoben)

21. Discussion / Outlook • Further Experimental Evaluation • Multiclass Categorisation • Combination with other Types of Regions Andreas Opelt (Graz University of Technology and University of Leoben)

22. Conclusion • Generic object recognition • A new Framework • A new Learning Model • Good Results Andreas Opelt (Graz University of Technology and University of Leoben)

23. Thank you ! Generic object recognition; not an easy task! Thanks to the Lava Project and the FWF Project – FSP Cognitive Vision Andreas Opelt (Graz University of Technology and University of Leoben)