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Mean-Field Theory and Its Applications In Computer Vision6 1

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

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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 3

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

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

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Joint stereo-object formulation 6 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

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

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

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

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

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

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Leuven dataset 12 Some of qualitative results

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Leuven dataset 13 Some of qualitative results AlgorithmTime (s)Object (% correct)Stereo (% correct) GC + Range (1)24.695.9476.97 GC + Range (2)49.995.9477.31 GC + Range (3)74.495.9477.46 Extended CostVol4.295.2077.18 Dense + HO (PLBF) 3.195.2478.89 Dense + HO (DTBF) 2.195.0678.21 Dense + HO + CostVol + DTBF 6.394.9879.00

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PP Test Review Sections 6-1 to 6-6

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