stereo matching information permeability for stereo matching – cevahir cigla and a.aydın alatan...

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Stereo Matching

• Information Permeability For Stereo Matching– Cevahir Cigla and A.Aydın Alatan – Signal Processing: Image Communication, 2013

• Radiometric Invariant Stereo Matching Based On Relative Gradients – Xiaozhou Zhou and Pierre Boulanger– International Conference on Image Processing (ICIP), IEEE 2012

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Outline

• Introduction• Related Works• Methods• Conclusion

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Introduction

• Goal – Get accurate disaprity maps effectively.– Find more robust algorithm, especially refinement

technique.

• Foucus : Refinement step and Comparison

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Related Works

• Stereo Matching– The same object, the same disparity• Segmentation• Calculate correspond pixels similarity

(color and geographic distance)

– Occlusion handling• Refinement

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Related Works

• Global Methods– Energy minimization

process

(GC,BP,DP,Cooperative)– Per-processing– Accurate but slow

• Local Methods– A local support region

with winner take all– Fast but inaccurate.– Adaptive Support Weight

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Related Works

Disparity Refinement

Disparity Optimization

Cost Aggregation

Matching Cost Computation

• Local methods algorithm

[1] D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms.International Journal of Computer Vision (IJCV), 47:7–42, 2002.

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• Edge Preserving filter : Remove noise and preserve structure/edge, like object consideration. Adaptive Support Weight [3] Bilateral filter(BF) [34] Guided filter(GF) [5] Geodesic diffusion [33] Arbitrary Support Region [39]

Related Works

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Reference Papers

[3] Kuk-JinYoon, InSoKweon, Adaptive support weight approach for correspondence search, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006.

[5] C. Rhemann, A. Hosni, M. Bleyer, C. Rother, M. Gelautz, Fast cost-volume filtering for visual correspondence and beyond, CVPR 2011.

[33] L. De-Maetzu, A. Villanueva nad, R. Cabeza, Near real-time stereo matching using geodesic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012.

[34] A. Ansar, A. Castano, L. Matthies, Enhanced real time stereo using bilateral filtering, in: Proceedings of the International Symposium on 3D Data Processing Visualization and Transmission, 2004.

[39] X. Mei, X Sun, M Zhou, S. Jiao, H. Wang, Z. Zhang, On building an accurate stereo matching system on graphics hardware, in: Proceed- ings of GPUCV 2011.

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Information Permeability For Stereo Matching

Method A.

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Methods A.

• Goal : Get high quality but low complexity

Save memory

Real-time application

• Successive Weighted Summation (SWS)– Constant time filtering + Weighted aggregation

◎Qingqing Yang, Dongxiao Li, Lianghao Wang, and Ming Zhang, “Full-Image Guided Filtering for Fast Stereo Matching”, Signal Processing Letters, IEEE March 2013 http://www.camdemy.com/media/7110

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Methods A.

• Cost Computation

Census Transform

1 1 0 0 0

1 1 0 0 0

1 1 X 0 0

0 0 0 1 1

1 1 1 1 1

121 130 26 31 39

109 115 33 40 30

98 102 78 67 45

47 67 32 170 198

39 86 99 159 210

1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1

Census transform window :

Census Hamming Distance

• Left image

• Right image

Hamming Distance = 3

1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1

1 1 1 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 1 1 1 1 1 1

XOR

0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0

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Methods A.

• Cost Computation

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Methods A.

• Cost Aggregation

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Methods A.

• Cost Aggregation

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Methods A.

(b)Horizontal effective weights (c)Vertical effective weights (d)2D effective weights

18(a) AW [3]

(b) Geodesic support [12]

(c) Arbitrary support region [4]

(d) Proposed

ComparisonWith

Other Methods

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Methods A.

• Refinement– Using cross-check to detect reliable and occluded

region detection ф is a constant (set to 0.1 throughout experiments)

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Methods A.

(a) Linear mapping function for reliable pixels based on disparities

(b)The resultant map for the left image

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Disparity Variation

BeforeAfter

0 <=> 1.151 <=> 1.302 <=> 1.453 <=> 1.604 <=> 1.755 <=> 1.906 <=> 2.057 <=> 2.208 <=> 2.359 <=> 2.50

10 <=> 2.6511 <=> 2.8012 <=> 2.9513 <=> 3.1014 <=> 3.2515 <=> 3.4016 <=> 3.5517 <=> 3.7018 <=> 3.8519 <=> 4

20 <=> 4.1521 <=> 4.3022 <=> 4.4523 <=> 4.6024 <=> 4.7525 <=> 4.9026 <=> 5.0527 <=> 5.2028 <=> 5.3529 <=> 5.50

30 <=> 5.6531 <=> 5.8032 <=> 5.9533 <=> 6.1034 <=> 6.2535 <=> 6.4036 <=> 6.5537 <=> 6.7038 <=> 6.8539 <=> 7

40 <=> 7.1541 <=> 7.3042 <=> 7.4543 <=> 7.6044 <=> 7.7545 <=> 7.9046 <=> 8.0547 <=> 8.2048 <=> 8.3549 <=> 8.50

50 <=> 8.6551 <=> 8.8052 <=> 8.9553 <=> 9.1054 <=> 9.2555 <=> 9.4056 <=> 9.5557 <=> 9.7058 <=> 9.8559 <=> 10

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•

(b) Without occlusion handling, bright regions correspond to small disparities

(c) Detection of occluded and un-reliable regions

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Methods A.

(b) occlusion handling with no background favoring (c) the proposed occlusion handling

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Experimental Results A.

• Device : Core Duo 1.80 GHz 2G Ram CPU • Implemented in C++ • Parameter : (T, α, )=(15, 0.2, 8)

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Parameter of Method A.

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Experimental Results A.

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Experimental Results A.

6D + 4D *V.S.

129D + 21D *

10~15X

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Experimental Results A.

• Proposed method is the fastest method without any special hardware implementation among Top-10 local methods of the Middlebury test bench, as of February 2013.

31Proposed

O(1) AW

Guided filter

Geodesic support

Arbitrary shaped cross filter

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Experimental Results A.

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Computational times A.

Cost InitializationCost AggregationRefinementOthers

≈70~75%

≈20~25%

≈5%

≈84%Cost InitializationCost AggregationMinimizationRefinement

≈45%≈44%

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Error Analysis A.

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Comparison with Full-Image◎

Full-Image Proposed

Initialization AD + Gradient SAD + Census

Aggregation

Refinement 1.Cross checking (lowest disparity)2.Weighted median filter

1. Cross checking (normalized disparity)2. Median filter (background handling)

◎Qingqing Yang, Dongxiao Li, Lianghao Wang, and Ming Zhang, “Full-Image Guided Filtering for Fast Stereo Matching”, Signal Processing Letters, IEEE

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Comparison with Full-Image

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Full-Image Results

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Full-Image Results

Proposed Results

Ground Truth

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Comparison with Full-Image

• My Experimental Results (SAD+Gradient)

• Lowest V.S. Normalized disparity

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Radiometric Invariant Stereo Matching Based On Relative

Gradients

Method B.

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Methods B.

• Goal : Adapt different environmental factors.(Illumination condition)

Effective and robust algorithm

• Relative gradient algorithm + Gaussian weighted function

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Background

• Lighting Model : – View independent, body reflection

•

•

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Background

•

•

• Lighting Model :

•

ANCC

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Method B.

• Cost Computation–

–

–

(i,j)

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Method B.

• Cost Aggregation–

• Refinement–

– Avoid White and black noises

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Experimental Results B.

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Experimental Results B.

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Experimental Results B.

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Experimental Results B.

• My Experimental Results (SAD+Gradient)• Original V.S.Rerange disparity

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Experimental Results B.

• Using related gradient intialization

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Conclusion

Initialization

ADc/SADc

ADg

C-Census

G-Census

???

Aggregation

Weighted-Window

Permeability

Cost-Filter

Arbitrary Support Region

???

Refinement

Lowest Neighbor

Normalizes

Re-Range

Scan-line

???