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POSE–CUTSimultaneous Segmentation and 3D Pose
Estimation of Humans using Dynamic Graph Cuts
Mathieu Bray Pushmeet Kohli Philip H.S. Torr
Department of Computing
Oxford Brookes University
Objective
Image Segmentation Pose Estimate
[Images courtesy: M. Black, L. Sigal]
Outline
Image Segmentation Problem Pose-Specific Segmentation The Pose Inference Problem Optimization Results Conclusion and Future Work
Outline
Image Segmentation Problem Pose-Specific Segmentation The Pose Inference Problem Optimization Results Conclusion and Future Work
The Image Segmentation ProblemSegments
Image
Problem – MRF Formulation
Notation• Labelling x over the set of pixels• The observed pixel intensity values y (constitute data D)
Energy E (x) = - log Pr(x|D) + constant
Unary term• Likelihood based on colour
Pairwise terms• Prior• Contrast term
Find best labelling x* = arg min E(x)
MRF for Image Segmentation
D (pixels)
x (labels)
Image Plane
i
j
xi
xj Unary Potential
i(D|xi)
Pairwise Potential
ij(xi, xj)
xi = {segment1, …, segmentk} for instance {obj, bkg}
Can be solved using graph cuts
MRF for Image Segmentation
MAP SolutionPair-wise Terms
Contrast Term
IsingModel
Data (D) Unary likelihood
Unary likelihood
Maximum a-posteriori (MAP) solution x* =
MRF for Image Segmentation
Pair-wise Terms MAP SolutionUnary likelihoodData (D)
Unary likelihood
Contrast Term
Uniform Prior
Maximum-a-posteriori (MAP) solution x* =
Need for a human like segmentation
Outline
Image Segmentation Problem Pose-Specific Segmentation The Pose Inference Problem Optimization Results Conclusion and Future Work
Shape-Priors and Segmentation
OBJ-CUT [Kumar et al., CVPR ’05]– Shape-Prior: Layered Pictorial Structure (LPS)– Learned exemplars for parts of the LPS model– Obtained impressive results
Layer 2Layer 1Spatial Layout
(Pairwise Configuration)
+ =
Shape-Priors and Segmentation
OBJ-CUT [Kumar et al., CVPR ’05]– Shape-Prior: Layered Pictorial Structure (LPS)– Learned exemplars for parts of the LPS model– Obtained impressive results
Shape-Prior Colour + ShapeUnary likelihoodcolour
Image
Problems in using shape priors
Intra-class variability• Need to learn an
enormous exemplar set• Infeasible for complex
subjects (Humans)
Multiple Aspects?
Inference of pose parameters
Do we really need accurate models?
Interactive Image Segmentation [Boykov & Jolly, ICCV’01]• Rough region cues sufficient • Segmentation boundary can be extracted from edges
additional segmentation
cues
user segmentation cues
Do we really need accurate models? Interactive Image Segmentation
• Rough region cues sufficient • Segmentation boundary can be extracted from edges
Rough Shape Prior - The Stickman Model
26 degrees of freedom• Can be rendered extremely efficiently• Over-comes problems of learning a huge exemplar set• Gives accurate segmentation results
Pose-specific MRF Formulation
D (pixels)
x (labels)
Image Plane
i
j
xi
xj Unary Potential
i(D|xi)
Pairwise Potential
ij(xi, xj)
(pose parameters)
Unary Potentiali(xi|)
Pose-specific MRFEnergy to be
minimizedUnary term
Shape prior
Pairwise potential
Potts model
distance transform
Pose-specific MRFEnergy to be
minimizedUnary term
Shape prior
Pairwise potential
Potts model
+ =
Shape Prior
MAP Solution
Colourlikelihood
Data (D) colour+shape
What is the shape prior?Energy to be
minimizedUnary term
Shape prior
Pairwise potential
Potts model
How to find the value of
ө?
Outline
Image Segmentation Problem Pose-Specific Segmentation The Pose Inference Problem Optimization Results Conclusion and Future Work
Formulating the Pose Inference Problem
Formulating the Pose Inference Problem
Resolving ambiguity using multiple views
Pose specific Segmentation Energy
Outline
Image Segmentation Problem Pose-Specific Segmentation The Pose Inference Problem Optimization Results Conclusion and Future Work
Solving the Minimization ProblemSolving the Minimization Problem
Minimize F(ө) using Powell Minimization
To solve:
Let F(ө) =
Computational Problem:
Each evaluation of F(ө) requires a graph cut to be computed. (computationally expensive!!) BUT..
Solution: Use the dynamic graph cut algorithm [Kohli&Torr, ICCV 2005]
Dynamic Graph Cuts
PB SB
cheaperoperation
computationally
expensive operation
Simplerproblem
PB*
differencesbetweenA and B
A and Bsimilar
PA SA
solve
Dynamic Graph Cuts
20 msec
Simplerproblem
PB*
differencesbetweenA and B
A and Bsimilar
xasolve
xb400 msec
Outline
Image Segmentation Problem Pose-Specific Segmentation The Pose Inference Problem Optimization Results Conclusion and Future Work
Segmentation Results
Colour +Smoothness
Colour + Smoothness+ Shape Prior
Only Colour
Image
[Images courtesy: M. Black, L. Sigal]
Segmentation Results - Accuracy
Information used
% of object pixels correctly
marked
Accuracy(% of pixels correctly
classified)
Colour 45.73 95.2
Colour + GMM 82.48 96.9
Colour + GMM + Shape
97.43 99.4
Segmentation + Pose inference
[Images courtesy: M. Black, L. Sigal]
Segmentation + Pose inference
[Images courtesy: Vicon]
Outline
Image Segmentation Problem Pose-Specific Segmentation The Pose Inference Problem Optimization Results Conclusion and Future Work
Conclusions
• Efficient method for using shape priors for object-specific segmentation
• Efficient Inference of pose parameters using dynamic graph cuts
• Good segmentation results
• Pose inference- Needs further evaluation- Segmentation results could be used for silhouette intersection
Future Work
• Use dimensionality reduction to reduce the number of pose parameters.
- results in less number of pose parameteres to optimize- would speed up inference
• Use of features based on texture
• Appearance models for individual part of the articulated model (instead of using a single appearance model).
Thank You