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Mean-Field Theory and Its Applications In Computer Vision2 1.

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Presentation on theme: "Mean-Field Theory and Its Applications In Computer Vision2 1."— Presentation transcript:

1 Mean-Field Theory and Its Applications In Computer Vision2 1

2 Problem Formulation 2 Grid CRF construction Grid CRF leads to over smoothing around boundaries Dense CRF is able to recover fine boundaries Dense CRF construction

3 Long Range Interaction 3 Able to recover proper flow for objects Teddy arms recovered using Global interaction image Local interactionGlobal interaction Ground truth Optical flow

4 Marginal Update 4 Marginal Update for large neighbourhood: Very Expensive Step (O(n 2 ))

5 Inference in Dense CRF 5 Time complexity increases Neighbourhood size MCMC takes 36 hours on 50K variables Graph-cuts based algorithm takes hours

6 Inference in Dense CRF 6 Time complexity increases Neighbourhood size MCMC takes 36 hours on 50K variables Graph-cuts based algorithm takes hours Not practical for vision applications

7 Inference in Dense CRF 7 Time complexity increases Neighbourhood size MCMC takes 36 hours on 50K variables Graph-cuts based algorithm takes hours Filter-based Mean-field Inference takes 0.2 secs Possibility of development of many exciting vision applications

8 Efficient inference 8 Assume Gaussian pairwise weight Label compatibility function

9 Efficient inference 9 Assume Gaussian pairwise weight Mixture of Gaussians Bilateral Spatial

10 Bilateral filter 10 outputinput reproduced from [Durand 02] outputinput

11 Marginal update 11 Assume Gaussian pairwise weight

12 How does it work 12 Very Expensive Step (O(n 2 ))

13 Message passing from all Xj to all Xi 13 Accumulates weights from all other pixels except itself

14 Message passing from all Xj to all Xi 14 Convert as Gaussian filtering step: Accumulate weights from all other pixels except itself

15 Message passing from all Xj to all Xi 15 Convert as Gaussian filtering step: Accumulate weights from all other pixels except itself

16 Efficient filtering steps 16 Now discuss how to do efficient filtering step


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