Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013 M.S. Student, Hee-Jong Hong...

Joint Histogram Based Cost Aggregationfor Stereo Matching - TPAMI 2013

M.S. Student, Hee-Jong HongSep 24, 2013

Dongbo Min, Member, IEEE, Jiangbo Lu, Member, IEEE,

Minh N. Do, Senior Member, IEEE

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• Introduction• Related Works• Proposed Method

: Improve Cost Aggregation• Experimental Results• Conclusion

Outline

Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

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Introduction


• Goal： Perform efficient cost aggregation.• Solution : Joint histogram + reduce redundancy • Advantage : Low complexity but keep high-quality.

Cost Initial-ization≈70~75%

≈20~25%

≈5%

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Related Works


• Complexity of aggregation： O(NBL)

• Reduce complexity approach• Scale image : Multi Scale Approach

D. Min and K. Sohn, “Cost aggregation and occlusion handling with WLS in stereo matching,” IEEE Trans. on Image Processing, 2008.

• Bilateral filter : Bilateral Approximation C. Richardt, D. Orr, I. P. Davies, A. Criminisi, and N. A. Dodgson, “Real-time spatiotemporal stereo matching using the dual-cross- bilateral grid,” in European Conf. on Computer Vision, 2010S. Paris and F. Durand, “A fast approximation of the bilateral filter using a signal processing approach,” International Journal of Computer Vision, 2009.

• Guided filter : Run in constant time => O(NL)C.Rhemann,A.Hosni,M.Bleyer,C.Rother,andM.Gelautz,“Fast cost-volume filtering for visual correspondence and beyond,” in IEEE Conf. on Computer Vision and Pattern Recognition, 2011

N : all pixels (W*H)B : window sizeL : disparity level

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Proposed Method

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Local Method Algorithm


• Cost initialization : Truncated Absolute Difference

=>• Cost aggregation : Weighted filter

• Disparity computation : Winner take all

[4,8]

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Improve Cost Aggregation


• New formulation for aggregation• Remove normalization• Joint histogram representation

• Compact representation for search range• Reduce disparity levels

• Spatial sampling of matching window• Regularly sampled neighboring pixels• Pixel-independent sampling

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New formulation for aggregation


• Remove normalization

=>

• Joint histogram representation

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Compact Search Range


• Cost aggregation

=>

• MC(q)： a subset of disparity levels whose size is Dc.

O( NBD )

O( NBDc )

N : all pixels (W*H)B : window sizeD : disparity level

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Compact Search Range


• Non-occluded region of ‘Teddy’ image Dc = 60

Final Accuracy = 93.7%

Dc = 6Final Accuracy =

94.1%

Dc = 5 (Best)Final Accuracy =

94.2%

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Spatial Sampling of Matching Window


• Reason• A large matching window and a well-defined weighting function leads to high

complexity.• Pixels should aggregate in the same object, NOT in the window.

• Solution• Color segmentation => Time consuming (Heavy)• Spatial Sampling => Easy but powerful• Regular Sampling => Independent from reference pixel => Reduce Complexity

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Spatial Sampling of Matching Window


• Cost aggregation

=>

• S : sampling ratio

O( NBDc )

O( NBDc / S2)

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Parameter defini-tionN : size of image B : size of matching win-dow N(p)=W×WMD : disparity levels size=DMC : The subset of dispar-ity size=DC<<DS : Sampling ratio

Pre-procseeing

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Experimental Result

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Experimental Results


• Pre-processing• 5*5 Box filter

• Post-processing• Cross-checking technique• Weighted median filter (WMF)

• Device： Intel Xeon 2.8-GHz CPU (using a single core only) and a 6-GB RAM• Parameter setting

( ) = (1.5, 1.7, 31*31, 0.11, 13.5, 2.0)

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(a) (b)

(c) (d)

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• Using too large box windows (7×7, 9×9) deteriorates the quality, and incurs more computational overhead.

• Pre-filtering can be seen as the first cost aggregation step and serves the removal of noise.

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Fig. 5. Performance evaluation: average per-cent (%) of bad matching pixels for ‘nonocc’, ‘all’ and ‘disc’ regions according to Dc and S.

2 better than 1

The smaller S, the better

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The smaller S, the longer

The bigger Dc, the longer

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• APBP : Average Percentage of Bad Pixels

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Ground truthError mapsResultsOriginal images

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Conclusion

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Conclusion


• Contribution• Re-formulate the problem with the relaxed joint histogram.• Reduce the complexity of the joint histogram-based aggregation.• Achieved both accuracy and efficiency.

• Future work• More elaborate algorithms for selecting the subset of label hypotheses.• Estimate the optimal number Dc adaptively.• Extend the method to an optical flow estimation.

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Thank you!

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