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

Mean-Field Theory and Its Applications In Computer Vision6

1

Page 2: Mean-Field Theory and Its Applications In Computer Vision6 1.

Inference In Product Label Space

• Many problem requires jointly estimating labels in product label space

2

Black Box

SolverLeft Camera Image

Right Camera Image

Object Class Segmentation

Dense Stereo Reconstruction

Page 3: Mean-Field Theory and Its Applications In Computer Vision6 1.

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

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Page 4: Mean-Field Theory and Its Applications In Computer Vision6 1.

Joint stereo-object inference• Introduce two different set of variables

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disparity variable

object variable

Messages exchanged between the variables

Page 5: Mean-Field Theory and Its Applications In Computer Vision6 1.

Joint stereo-object formulation

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Unary Potential

• Weighted sum of object class, depth and joint potential

• Joint unary potential based on histograms of height

Page 6: Mean-Field Theory and Its Applications In Computer Vision6 1.

Joint stereo-object formulation

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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

Page 7: Mean-Field Theory and Its Applications In Computer Vision6 1.

Joint stereo-object formulation

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Higher Order Potential

• Use higher order terms only for object variables

Page 8: Mean-Field Theory and Its Applications In Computer Vision6 1.

Joint stereo-object updation

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For object variables

Message from disparity variables to object variables

Page 9: Mean-Field Theory and Its Applications In Computer Vision6 1.

Joint stereo-object updation

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For object variables

Filtering is done using permutohedral lattice based filtering strategy

Page 10: Mean-Field Theory and Its Applications In Computer Vision6 1.

Joint stereo-object updation

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For disparity variables

Message from object variables to disparity variables

Page 11: Mean-Field Theory and Its Applications In Computer Vision6 1.

Joint stereo-object updation

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For disparity variables

Filtering is done using domain transform based filtering strategy

Page 12: Mean-Field Theory and Its Applications In Computer Vision6 1.

Leuven dataset

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Some of qualitative results

Page 13: Mean-Field Theory and Its Applications In Computer Vision6 1.

Leuven dataset

13Some of qualitative results

Algorithm Time (s) Object (% correct) Stereo (% correct)

GC + Range (1) 24.6 95.94 76.97

GC + Range (2) 49.9 95.94 77.31

GC + Range (3) 74.4 95.94 77.46

Extended CostVol 4.2 95.20 77.18

Dense + HO(PLBF)

3.1 95.24 78.89

Dense + HO(DTBF)

2.1 95.06 78.21

Dense + HO + CostVol + DTBF

6.3 94.98 79.00