Mean-Field Theory and Its Applications In Computer Vision6

1. Mean-Field Theory and Its Applications In Computer Vision6

2. Inference In Product Label Space • Many problem requires jointly estimating labels in product label space Black Box Solver Left Camera Image Object Class Segmentation Right Camera Image Dense Stereo Reconstruction

3. Joint Object-Stereo Labelling • Computation complexity very high • Graph-cuts based method takes almost 50 secs for 320x200 image size • We propose mean-field based inference method • Our method takes 2 secs for the same task

4. Joint stereo-object inference • Introduce two different set of variables disparity variable object variable Messages exchanged between the variables

5. Joint stereo-object formulation Unary Potential • Weighted sum of object class, depth and joint potential • Joint unary potential based on histograms of height

6. Joint stereo-object formulation Pairwise Potential • Object class and depth edges correlated • We disregard the joint pairwise terms though • Dense pairwise connection at both disparity variable and object variables

7. Joint stereo-object formulation Higher Order Potential • Use higher order terms only for object variables

8. Joint stereo-object updation For object variables Message from disparity variables to object variables

9. Joint stereo-object updation For object variables Filtering is done using permutohedral lattice based filtering strategy

10. Joint stereo-object updation For disparity variables Message from object variables to disparity variables

11. Joint stereo-object updation For disparity variables Filtering is done using domain transform based filtering strategy

12. Leuven dataset Some of qualitative results

13. Leuven dataset Some of qualitative results