Joint histogram based cost aggregation for stereo matching tpami 2013
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Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013. Dongbo Min, Member, IEEE , Jiangbo Lu, Member, IEEE , Minh N. Do, Senior Member, IEEE . M.S. Student, Hee -Jong Hong Sep 24, 2013. Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013.

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Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

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Joint histogram based cost aggregation for stereo matching tpami 2013

Joint Histogram Based Cost Aggregationfor Stereo Matching - TPAMI 2013

Dongbo Min, Member, IEEE,

Jiangbo Lu, Member, IEEE,

Minh N. Do, Senior Member, IEEE

M.S. Student, Hee-Jong Hong

Sep 24, 2013


Outline

Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

Outline

  • Introduction

  • RelatedWorks

  • Proposed Method:Improve CostAggregation

  • ExperimentalResults

  • Conclusion


Introduction

Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

Introduction

  • Goal:Perform efficient cost aggregation.

  • Solution : Jointhistogram+reduceredundancy

  • Advantage : Low complexitybutkeephigh-quality.


Related works

Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

Related Works

N : all pixels (W*H)

B : window size

L : disparity level

  • Complexityofaggregation:O(NBL)

  • Reducecomplexityapproach

    • Scaleimage: Multi Scale ApproachD. Min and K. Sohn, “Cost aggregation and occlusion handling with WLS in stereo matching,” IEEE Trans. on Image Processing, 2008.

    • Bilateralfilter: 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.

    • Guidedfilter: 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


Joint histogram based cost aggregation for stereo matching tpami 2013

Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

Proposed Method


Local method algorithm

Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

Local Method Algorithm

  • Cost initialization : Truncated Absolute Difference

    =>

  • Cost aggregation : Weighted filter

  • Disparity computation : Winner take all

[4,8]


Improve cost aggregation

Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

Improve CostAggregation

  • 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


  • New formulation for aggregation

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    New formulation for aggregation

    • Remove normalization

      =>

    • Joint histogram representation


    Compact search range

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    Compact Search Range

    • Cost aggregation

      =>

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

    N : all pixels (W*H)

    B : window size

    D : disparity level

    O( NBD )

    O( NBDc )


    Compact search range1

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    Compact Search Range

    • Non-occluded region of ‘Teddy’ image

    Dc= 60

    Final Accuracy = 93.7%

    Dc= 6

    Final Accuracy = 94.1%

    Dc= 5 (Best)

    Final Accuracy=94.2%


    Spatial sampling of matching window

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    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


    Spatial sampling of matching window1

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    Spatial Sampling of Matching Window

    • Cost aggregation

      =>

      • S : sampling ratio

    O( NBDc )

    O( NBDc / S2)


    Joint histogram based cost aggregation for stereo matching tpami 2013

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    Parameter definition

    N : size of image

    B : size of matching window

    N(p)=W×W

    MD : disparity levels

    size=D

    MC : The subset of disparity

    size=DC<<D

    S : Sampling ratio

    Pre-procseeing


    Joint histogram based cost aggregation for stereo matching tpami 2013

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    Experimental Result


    Experimental results

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    ExperimentalResults

    • Pre-processing

      • 5*5 Box filter

    • Post-processing

      • Cross-checking technique

      • Weightedmedian filter (WMF)

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

    • Parametersetting

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


    Experimental results1

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    ExperimentalResults

    (a)

    (b)

    (c)

    (d)


    Experimental results2

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    ExperimentalResults

    • 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.


    Experimental results3

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    ExperimentalResults

    The smaller S, the better

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

    2 better than 1


    Experimental results4

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    ExperimentalResults

    The smaller S, the longer

    The bigger Dc, the longer


    Experimental results5

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    ExperimentalResults

    • APBP : Average Percentage of Bad Pixels


    Experimental results6

    Original images

    Results

    Error maps

    Ground truth

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    ExperimentalResults


    Experimental results7

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    ExperimentalResults


    Joint histogram based cost aggregation for stereo matching tpami 2013

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    Conclusion


    Conclusion

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    Conclusion

    • Contribution

      • Re-formulate the problem withthe relaxed joint histogram.

      • Reduce the complexity of the joint histogram-based aggregation.

      • Achieved both accuracy and efficiency.

    • Futurework

      • Moreelaborate algorithms for selecting the subset of label hypotheses.

      • Estimate the optimal number Dc adaptively.

      • Extendthemethodtoanopticalflowestimation.


    Joint histogram based cost aggregation for stereo matching tpami 2013

    Thank you!


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