mulitiview photometric stereo - waseda...

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画像領域・対応点推定問題へのグラフカットの適用

Tatsunori TANIAIThe University of Tokyo

CRESET Symposium on MRF and Deep Learningat Waseda University

January 13, 2016

Solving Segmentation and Dense CorrespondenceProblems using Graph Cuts

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Self-Introduction

Tatsunori TANIAI / 谷合竜典 (2nd year of PhD course at the University of Tokyo)

• Specialties: Optimization and its applications in computer vison

• Personal history

PhD

Master

Bachelor

Kosen

2014.4

2012.4

2009.4

National Institute of Technology, Tokyo College (東京高専)

Research Internship at Microsoft Research Asia (Advisor: Dr. Yasuyuki Matsushita)

Research Internship at Microsoft Research Redmond (Advisor: Dr. Sudipta Sinha)

Transferred to the University of Tokyo

Microsoft Research Asia Fellow 2015

Joined Naemura Laboratory (University of Tokyo)

Joined Yoichi Sato Laboratory (University of Tokyo) / JSPS Young Research Fellow

Now

Graduation(expected)

2017.4

3

Collaborators

Prof. Yoichi Satoat Univ. of Tokyo

(Ph.D. advisor)

Prof. Yasuyuki Matsushitaat Osaka Univ.

(mentor at MSRA)

Dr. Sudipta Sinhaat MSR Redmond(mentor at MSR)

Prof. Takeshi Naemuraat Univ. of Tokyo

(Bachelor-Master advisor)

4

Before going on to my talk…

• My talk today is only about “my” past and on-going projects

– Higher-order MRF optimization for low level vision [CVPR 2015]

– Continuous MRF optimization for stereo matching [CVPR 2014] [Submitted to PAMI]

– Joint dense correspondence and cosegmentation [Submitted to CVPR 2016]

• My talk contains confidential information

• PLEASE do not expect too much about the deep learning part

5

Overview

• Introduction

• Higher-order MRF optimization for low level vision [CVPR 2015]

• Continuous MRF optimization for stereo matching [CVPR 2014]

• Joint dense correspondence and cosegmentation [on-going]

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MAP-MRF Inference

Unknownforeground mask

Observed data

Observation likelihood Prior

max𝑋

𝑃 𝑋|𝐷 = 𝑃 𝐷|𝑋 𝑃 𝑋 / 𝑃(𝐷)

− log ⋅

min𝑋

𝐸 𝑋 = 𝜙 𝐷|𝑋 + 𝜓 𝑋

Probability

Energy

?

Color distributionsfrom user scribbles

Spatialsmoothness

= 𝑖𝜙𝑖 𝑋𝑖 + 𝑖𝑗𝜙𝑖𝑗 𝑋𝑖 , 𝑋𝑗 + 𝐶𝜙𝐶 𝑋𝐶

Pairwise/1st order MRF

Unary Pairwise Higher-order

𝑋 = 1

𝑋 = 0

Posteriori

Higher-order MRF

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Important Properties in MAP-MRF by Graph Cuts

Submodular(discrete convexity)

Pairwise(function form)

Binary(label space)

Yakusoku-no-chi約束の地

(optimally solved)

Non-submodular(discrete non-convexity)

Higher-order(function form)

Multi-label(label space)

Make problems easier Make problems harder

Decompose/convert hard problems into easy problems.

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Graph Cuts for MAP-MRF Inference

Optimal inference of binary MRFs by graph cuts [Kolmogorov & Zabih, PAMI 04]

• Pairwise: 𝐸 𝑋 = 𝜙𝑖 𝑋𝑖 + 𝜙𝑖𝑗 𝑋𝑖 , 𝑋𝑗

• Binary: 𝑋𝑖 ∈ {0,1}

• Submodular: 𝜙 0,0 + 𝜙 1,1 ≤ 𝜙 1,0 + 𝜙 0,1

Sinkterminal

Sourceterminal

min-cut

Max-flow min-cut problem

Equivalent

e.g.) Potts model 𝜙 𝑋𝑖 , 𝑋𝑗 = 𝑋𝑖 − 𝑋𝑗

Approximate inference of multi-label MRFs by α-expansion algorithm [Boykov+, PAMI 01]

• Pairwise: 𝐸 𝑋 = 𝜙𝑖 𝑋𝑖 + 𝜙𝑖𝑗 𝑋𝑖 , 𝑋𝑗• Multi-label: 𝑋 ∈ {0,1,⋯ , 𝐾}

for each 𝛼 ∈ {0,1,⋯ , 𝐾}𝑋𝑡+1 = arg min 𝐸(𝑋) where 𝑋𝑖 ∈ {𝑋𝑖

𝑡 , 𝛼}

●move●move●move●move●move●move●move

Image coutesty: N.Komodakis, P.Torr, V.Kolmogorov, Y.Boykov “Discrete Optimizationin Computer Vision”, Tutorial at ICCV 2007

Solved by GC

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Overview

• Introduction




Non-Submodular

Higher-orderBinary

Non-Submodular

PairwiseBinary

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Binary Energy Minimization

Binary variables in two forms:

• Find a binary labeling: 𝑠𝑝 ∈ 0,1

• Find a region: 𝑆 = 𝑝 | 𝑠𝑝 = 1

Image segmentation Image binarization

𝑆

𝑠𝑝 = 1

𝑠𝑝 = 0Ω

𝐸 𝑆 = 𝑄 𝑆 + 𝑅 𝑆

Quadratic Higher-order (our focus)

= 𝑝𝑚𝑝𝑠𝑝 + 𝑝𝑞𝑚𝑝𝑞𝑠𝑝𝑠𝑞 + 𝑅 𝑆

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Approximation Approach

How to find good linear coefficients 𝒉𝒑?

• Gradient descent approach

• Bound optimization approach (our focus)

𝐸 𝑆 = 𝑄 𝑆 + 𝑅 𝑆Quadratic Higher-order

≃ 𝑄 𝑆 + ℎ, 𝑆 ℎ, 𝑆 = 𝑝 ℎ𝑝𝑠𝑝where

Linear approximation

1. Approximate 𝑹 𝑺 by a linear function:

𝑆𝑡+1 = argmin𝑆 𝐸 𝑆

2. Minimize approximated 𝑬 𝑺 by graph cuts:

Until convergence

Repeat

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Ener

gy

Solution space

Taylor-based linear approximation

Fast Trust Region [Gorelick+ CVPR ’13, ‘14]

Gradient Descent Approach

+ Trust region 𝑆 − 𝑆𝑡 < 𝜏

𝑆𝑡

𝑆𝑡+1

Locally approximates 𝐸(𝑆) at current 𝑆𝑡

May worsen solutions

𝐸(𝑆)

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Ener

gy

Solution space

Our Approach (Bound Optimization)

𝑆𝑡

𝑆𝑡+1

Globally approximates 𝐸(𝑆) using current 𝑆𝑡

Never worsens solutions: 𝐸 𝑆𝑡+𝑡 ≤ 𝐸(𝑆𝑡)

Piecewise-linear upper-bounds

updated in a coarse-to-fine manner.

𝐸 𝑆|𝑆𝑡 ≥ 𝐸(𝑆)

𝐸 𝑆|𝑆𝑡+1

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Contributions

• Achieve state-of-the-art performances

• Generalize previous bound optimization methods:

– Submodular Supermodular Procedure [Narasimhan+ UAI ‘05]

– Bhattacharyya Measure Graph Cuts [Ayed+ CVPR ‘10]

– Auxiliary Cuts [Ayed+ CVPR ‘13]

– Local Submodular Approximation (AUX) [Gorelick+ CVPR ‘14]

– Parametric Pseudo Bound Cuts [Tang+ ECCV ‘14]

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Overview

• Introduction


– Revising SSP

– Proposed method

– Experiments



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Submodular-Supermodular Procedure (SSP) [Narasimhan+, UAI05]

SSP: minimization method of supermodular functions

𝑅(𝑆)

Ω∅

𝑅 𝑆 = 𝑣0 − 𝑆 2

Area-size constraints

𝑅 𝑆 =

𝑧∈{𝑏𝑖𝑛𝑠}

ℎ𝑖𝑠𝑡𝑧0 − ℎ𝑖𝑠𝑡𝑍(𝑆)

𝑝

𝑳𝒑-dist. btw histograms

i.e. size constraints for each color binSupermodular(similar to convex func)

Minimization is NP-hard

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Greedy Approximation by Permutation

What if we know how likely each 𝑠𝑖 is “1”?

Lik

elih

oo

d

}

}

𝜎1 𝜎2 𝜎3 𝜎4 𝜎5𝑆 = {

𝑅(𝑆)

∅ Ω

ℎ = {

1. Permute nodes by their likelihoods

Then we can

2. Compute a linear approximation function

ℎ, 𝑆 ≃ 𝑅 𝑆as energy transitions along the permutation 𝜎.

Ordering 𝜎

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Permutation 𝜎 by Distance [Rother+, CVPR06]

Lik

elih

oo

d

𝜎1 𝜎2 𝜎3 𝜎4 𝜎5

𝑺𝒕

Distance from boundary

Really reliable?

Current S

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Overview

• Introduction


– Revising SSP

– Proposed method

– Experiments



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Grouped Permutation

• Ignore unreliable permutation 𝝈 by grouping• Use finer bounds as iterations proceed

𝜎1 𝜎2 𝜎3 𝜎4 𝜎5

Lik

elih

oo

d

Sort 𝜎

SSP [UAI 05]

𝜎1 𝜎2 𝜎3 𝜎4 𝜎5

Sort & group

Proposed AC [CVPR 13]

∞

= 𝑆𝑡

Not requirespermutation 𝜎

Always finestapproximation

Always coarsestapproximation

Adaptive“coarse-to-fine”

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Visual Comparison by 2 Variables

𝑬 𝑺 = 2 𝑠1 + 𝑠2 − 1 + 𝑠2

𝑠1𝑠2

SSP (with true perm. 𝜎)

𝜎: 𝑠1 → 𝑠2

SSP (with wrong perm. 𝜎)

𝜎: 𝑠2 → 𝑠1

Our grouped bound

Min

Grouping yields better bounds when 𝝈 is inaccurate

Tight only along 𝝈

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Overview

• Introduction


– Revising SSP

– Proposed method

– Experiments



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0%

1%

2%

3%

4%

5%

32 64 96 128 160 192

Err

or

Rate

Number of Bins per Channel

Image Segmentation Results

PROPOSED

PROPOSED

pPBC [Tang+ ECCV ‘14]

AC [Ayed+ CVPR ‘13]

FTR [Gorelick+ ECCV ‘13]

𝐸 𝑆 = 𝑄 𝑆 +

𝑧∈{𝑏𝑖𝑛𝑠}

ℎ𝑖𝑠𝑡𝑧 − ℎ𝑖𝑠𝑡𝑧(𝑆)2

INPUT: RGB color histogramlearned from ground truth.

Pairwisesmoothness term

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PROPOSED pPBC [Tang+ ECCV ‘14]

AC [Ayed+ CVPR ‘13] SSP [Narasimhan+ UAI ‘05]FTR [Gorelick+ ECCV ‘13]

Ground truth


L2-distance, RGB histograms with 643 bins

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Ground truth PROPOSED pPBC [Tang+ ECCV ‘14]

AC [Ayed+ CVPR ‘13] SSP [Narasimhan+ UAI ‘05]FTR [Gorelick+ ECCV ‘13]


L2-distance, RGB histograms with 643 bins

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Deconvolution

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Overview

• Introduction



• Joint dense correspondence and cosegmentation

Non-Convex

PairwiseContinuous

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Stereo Matching

𝑥

𝑦𝑧

𝑝′

Left

Right

Estimate depth z (or disparity) by maximizing patch similarity.

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Over-Parameterized Stereo Matching [Bleyer+, BMVC 2011]

𝑥

𝑦𝑧

𝑝′

Left

Right

Estimate local tangent planes (depth 𝒛 + normal 𝒏)by maximizing patch similarity.

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𝑑𝑞 = 𝑎𝑢 + 𝑏𝑣 + 𝑐

(or disparity plane: 𝑑𝑝 = 𝑎𝑝𝑢 + 𝑏𝑝𝑣 + 𝑐𝑝)

𝐸 𝑻 = 𝑝𝐷𝑝ℎ𝑜𝑡𝑜(𝑇𝑝)

Pairwise MRF formulation:

Over-parameterized Stereo Formulation

𝑇𝑝 =1 − 𝑎𝑝 −𝑏𝑝 −𝑐𝑝0 1 0

Over-parametrized disparity

Patch-based photo-consistency term[Blayer+ BMVC ‘11]

Curvature-based smoothness term[Olsson+ CVPR ‘13]

Minimize abs( ) + abs( ) to enforce piecewise linear disparity

Image coordinates 𝑢

𝑝 𝑞𝑑𝑝Dis

pa

rity

Estimate 𝑻𝒑(𝒂, 𝒃, 𝒄) for each of densely overlapping patches

via energy minimization on pairwise MRF models.

RL

+ 𝑝,𝑞𝑅𝑠𝑚𝑜𝑜𝑡ℎ(𝑇𝑝, 𝑇𝑞)

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Overview

• Introduction



– Proposed method

– Experiments

– Fast implementation


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Conventional α-expansions[Boykov+ TPAMI ‘02]

Local Expansion Moves

Spatially localizedlabel-space searching

Our local α-expansions

Fusion via graph cuts

Current solution

α

Intractable due to our infinite label space

ProposalsMany

α’sProposals

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Local Expansion Moves

Current solution Local α-expansion(disparity plane patch)

𝛼Choose Perturb𝑻𝒑 + 𝚫

Improved solution

Spatial propagation and randomized searchsimilarly to PatchMatch inference [Barnes+ ToG ‘09]

3x3 cells

Fusion via graph cuts

Current solution

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Overview

• Introduction



– Proposed method

– Experiments



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Results for Middlebury Benchmark

After 10 iterations

Disparity map

After post-proc.

(Error rates by 0.5-pixel error threshold)

Error map

1st Rank even without post-processing

White: correctBlack: incorrectGray: incorrect but occluded

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Overview

• Introduction



– Proposed method

– Experiments



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Parallelization of Local Expansion Moves

0 1 2 3 4 5 6 7

0 0 1 2 3 0 1 2 3

1 4 5 6 7 4 5 6 7

2 8 9 10 11 8 9 10 11

3 12 13 14 15 12 13 14 15

4 0 1 2 3 0 1 2 3

5 4 5 6 7 4 5 6 7

6 8 9 10 11 8 9 10 11

7 12 13 14 15 12 13 14 15

Cell index 𝑖

Ce

ll in

de

x 𝑗

0 1 2 3 4 5 6 7 8

0

1

2

3

4

5

6

7

8

Cell index 𝑖

Ce

ll in

de

x 𝑗

Divide into 16 groups of mutually-disjoint (parallelizable) local expansion moves.Computations of patch-matching cost (data term), min-cut, etc. can be done in parallel.

Scheduling (16 groups) Mutually-disjointlocal expansion moves

The region of a local expansion move.(3x3 cells)

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𝐼 𝐼′

𝑝𝐷𝑝ℎ𝑜𝑡𝑜(𝑇𝑝)

Fast Computation by Cost-Volume Filtering

Patch-based photo-consistency term

𝐼′𝐼

𝑝

Adaptive window(bilateral-filter weight)

𝑊

𝑊

Bottle-neck: naïve computation of each

matching cost 𝐷𝑝ℎ𝑜𝑡𝑜 𝑇𝑝 is 𝑂 𝑊

𝐷𝑝ℎ𝑜𝑡𝑜 𝑇𝑝 =

𝑞

𝑊𝑝𝑞 𝐼𝑞 − 𝐼𝑞′′

The region of a local expansion move

Union of matching windows (filtering region)

𝑊

Fast computation1. Compute raw matching costs of a filtering region2. Apply edge-aware constant-time filtering

e.g.) guided image filtering [He+ ECCV 10, PAMI 13]

Computation of 𝑫𝒑𝒉𝒐𝒕𝒐 𝑻𝒑 ≃ 𝑶 𝟏

PatchMatch filter [Lu+ CVPR 13]

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Running Time Comparison

0.62

0.63

0.64

0.65

0.66

0.67

0.68

0 100 200 300 400 500 600

Rela

tive E

nerg

y F

unction V

alu

e

Running Time [seconds]

LE-BF (CPUx1)

LE-BF (CPUx4)

LE-GF (CPUx1)

LE-GF (CPUx4)

LE-BF (GPU+CPUx4)

Fast cost-volume filtering (5.3x)

Until 1900s

1 to 4 CPU cores (3.5x)

4 CPU cores + GPU (19x)

40

Overview

• Introduction




Non-Convex

PairwiseContinuous

+ α

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Summary and References

• Higher-order MRF optimization for low level vision– Piecewise-linear approximation bounds updated in a coarse-to-fine manner

Taniai, Matushita, Naemura: “Superdifferential Cuts for Binary Energies” [CVPR 2015]

• Continuous MRF optimization for stereo matching– Local expansion moves for PatchMatch-like inference by GC (or originally “locally shared labels”)

Taniai, Matsushita, Naemura: “Graph Cut based Continuous Stereo Matching using Locally Shared Labels” [CVPR 2014]

– Fast implementation by parallelization and local cost-volume filtering Taniai, Matsushita, Sato, Naemura: [Submitted to PAMI]

• Joint dense correspondence and cosegmentation– Dynamic hierarchical regularization and two-pass optimization

Taniai, Sinha, Sato [Submitted to CVPR 2016]

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Thanks Again to My Collaborators

Prof. Yoichi Satoat Univ. of Tokyo

(Ph.D. advisor)

Prof. Yasuyuki Matsushitaat Osaka Univ.

(mentor at MSRA)

Dr. Sudipta Sinhaat MSR Redmond(mentor at MSR)

Prof. Takeshi Naemuraat Univ. of Tokyo

(Bachelor-Master advisor)

THANK YOU FOR LISTENING

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