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 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


Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

Outline

  • Introduction

  • RelatedWorks

  • Proposed Method:Improve CostAggregation

  • ExperimentalResults

  • Conclusion


Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

Introduction

  • Goal:Perform efficient cost aggregation.

  • Solution : Jointhistogram+reduceredundancy

  • Advantage : Low complexitybutkeephigh-quality.


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

Proposed Method


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]


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


  • Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    New formulation for aggregation

    • Remove normalization

      =>

    • Joint histogram representation


    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 )


    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%


    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


    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

    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

    Experimental Result


    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)


    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    ExperimentalResults

    (a)

    (b)

    (c)

    (d)


    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.


    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


    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    ExperimentalResults

    The smaller S, the longer

    The bigger Dc, the longer


    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    ExperimentalResults

    • APBP : Average Percentage of Bad Pixels


    Original images

    Results

    Error maps

    Ground truth

    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    ExperimentalResults


    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    ExperimentalResults


    Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

    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.


    Thank you!


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