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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

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 2013Outline
  • Introduction
  • RelatedWorks
  • Proposed Method:Improve CostAggregation
  • ExperimentalResults
  • Conclusion
introduction
Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013Introduction
  • Goal:Perform efficient cost aggregation.
  • Solution : Jointhistogram+reduceredundancy
  • Advantage : Low complexitybutkeephigh-quality.
related works
Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013Related 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
local method algorithm
Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013Local 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 2013Improve 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 2013New formulation for aggregation
  • Remove normalization

=>

  • Joint histogram representation
compact search range
Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013Compact 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 2013Compact 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 2013Spatial 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 2013Spatial Sampling of Matching Window
  • Cost aggregation

=>

    • S : sampling ratio

O( NBDc )

O( NBDc / S2)

slide13
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

experimental results
Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013ExperimentalResults
  • 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 results2
Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013ExperimentalResults
  • 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 2013ExperimentalResults

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 2013ExperimentalResults

The smaller S, the longer

The bigger Dc, the longer

experimental results6

Original images

Results

Error maps

Ground truth

Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013

ExperimentalResults
conclusion
Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013Conclusion
  • 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.
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