![Aim ,[object Object],[object Object],[object Object],[object Object],[object Object],Segmentation Object Category Model Cow Image Segmented Cow Slide from Kumar ‘05](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Cvpr2007 object category recognition p4 - combined segmentation and recognition
- 1. Part 4: Combined segmentation and recognition by Rob Fergus (MIT)
- 8. Jigsaw approach: Borenstein and Ullman, 2002
- 9. Implicit Shape Model - Liebe and Schiele, 2003 Liebe and Schiele, 2003, 2005 Backprojected Hypotheses Interest Points Matched Codebook Entries Probabilistic Voting Voting Space (continuous) Backprojection of Maxima Segmentation Refined Hypotheses (uniform sampling)
- 11. Generative Markov Random Field I (pixels) Image Plane i j Prior has no dependency on I h (labels) {foreground,background} h i h j Unary Potential i ( I |h i , i ) Pairwise Potential (MRF) ij (h i , h j | ij ) MRF Prior Likelihood
- 15. OBJCUT: Results In the absence of a clear boundary between object and background Segmentation Image Using LPS Model for Cow
- 16. Levin & Weiss [ECCV 2006] Segmentation alignment with image edges Consistency with fragments segmentation Resulting min-cut segmentation
- 18. Layout consistency Neighboring pixels (p,q) ? (p,q+1) (p,q) (p+1,q+1) (p-1,q+1) Layout consistent Winn and Shotton 2006 (8,3) (9,3) (7,3) (8,2) (9,2) (7,2) (8,4) (9,4) (7,4)
- 19. Layout Consistent Random Field Winn and Shotton 2006 Layout consistency Part detector
- 20. Stability of part labelling Part color key
- 21. Object-Specific Figure-Ground Segregation Stella X. Yu and Jianbo Shi, 2002
- 22. Image parsing: Tu, Zhu and Yuille 2003
- 23. Image parsing: Tu, Zhu and Yuille 2003
- 24. Segment out all the cars … . fused tree model for cars Unseen image Training images Segmented Cars Segmentation Trees Overview Multiscale Seg. Todorovic and Ahuja, CVPR 2006 Slide from T. Wu
- 25. LOCUS model Deformation field D Position & size T Class shape π Class edge sprite μ o , σ o Edge image e Image Object appearance λ 1 Background appearance λ 0 Mask m Shared between images Different for each image Kannan, Jojic and Frey 2004 Winn and Jojic, 2005
- 31. Object-Specific Figure-Ground Segregation Some segmentation/detection results Yu and Shi, 2002
- 35. Levin and Weiss 2006 Levin and Weiss, ECCV 2006
- 36. Results: horses
- 37. Results: horses
- 43. LayoutCRF
Notes de l'éditeur
- Different occlusions preserves ordering, deformations preserve ordering
- Different occlusions preserves ordering, deformations preserve ordering
- Edge weight larger at image edges
- Write down the contribution part of this paper
- Emphasise class model (shared) – all other variables per-image. Emphasise LEARN EVERYTHING SIMULTANEOUSLY.