1. Efficient Stereo Matching Based on a New Confidence Metric
Won-Hee Lee, Yumi Kim, and Jong Beom Ra
Department of Electrical Engineering, KAIST, Daejeon, Korea
20th European Signal Processing Conference (EUSIPCO 2012)

2. Outline Introduction Related Work Proposed Algorithm
Experimental Results
Conclusion

3. Introduction

4. Introduction
For the TV application, stereo matching should be performed in real-time.
Aggregation kernel size is to be small
Aggregation process takes large computation loads
May cause problems in a textureless area
Texture area information incorrect textureless area
Propose a new confidence metric for stereo matching
For efficient refinement (with small kernel size)
Objective:

5. Introduction
35X35
5X35
[4] K. Zhang, J. Lu, and G. Lafruit, “Cross-based local stereo matching using orthogonal integral images,” IEEE TCSVT, 2009.

6. Related Work

7. Related Work Cross-based stereo matching algorithm[4]
Raw matching cost:
Aggregated cost:
Ud(x) : local support region
Ud(x) : the number of pixels in Ud(x)

8. Related Work Cross-based stereo matching algorithm[4] Winner-take-all:
d0(x) : the initial disparity
dmax(x) : the maximum disparity

9. Related Work Confidence metrics[5]:
Several metrics were proposed to measure the confidence level of match
Utilizing:
Aggregated cost
Curvature of the cost curve
Left-right consistency
[5] X. Hu and P. Mordohai, “Evaluation of stereo confidence indoors and outdoors,” in CVPR, 2010

10. Confidence metrics 1) Matching score metric (MSM) C : aggregated cost
di : the disparity that reveals the ith minimum cost
White(High confidence)
Black(Low confidence)

11. Confidence metrics
2) Curvature of cost curve metric (CUR)

12. Confidence metrics
3) Naive peak ratio metric (PKRN)

13. Confidence metrics 4) Naive winner margin metric (WMNN)
computes a margin between two minimum costs
normalize it with the sum of total costs

14. Confidence metrics 5) Left right difference metric (LRD)
min{cR(x - d1, dR)}:
the minimum value of a cost curve at the corresponding pixel in the right image.

15. Proposed Algorithm

16. Framework

17. Proposed Confidence Metric
‧:Correct estimated pixels
X : Incorrect estimated pixels

18. Proposed Confidence Metric
The new metric is proposed as
Characteristic:
extracts the curvature information across a range larger than that including three cost values in the CUR metric
Ud(x) : improve the metric performance for a cost graph with a small curvature.
LoG : a Laplacian of Gaussian filter of n-taps

19. Refinement Weighted median filter Weight 𝒘 𝒊 :
𝒅 𝒊 𝟎 : the initial disparity of neighboring pixels (same color segment)
: duplication operator
offset
a slope of function

20. Refinement Histogram-based color segmentation algorithm[6]:
[6] J. Delon, A. Desolneux, J. L. Lisani, and A. B. Petro, “A nonparametric approach for histogram segmentation,” IEEE TIP, 2007.
Refinement
Histogram-based color segmentation algorithm[6]:

21. Refinement
The filtering is applied only to the limited number if pixels
Due to small size of filtering kernel
To enlarge the filtering range
Vertically propagate the filtered result of a current pixel
Propagation Data
After weighted median filtering…
Datapropagate = DataA
Filtered Disparity A
If Weightpropagate > WeightB
DisparityB = Disparitypropagate
Else
Datapropagate = DataB
Weight B
(current)
Color segment index C

22. Experimental Results

23. Experimental Results Parameters:
Aggregation kernel
Filtering kernel T n σ τ
5 x 35
5 x 63 60 7 10 2
n : Laplacian of Gaussian filter of n-taps
offset
a slope of function

24. Experimental Results
Initial disparity map
Bad pixel
Confidence map

25. AUC: Area Under the Curve
Experimental Results
AUC: Area Under the Curve
Venus
Tsukuba
Teddy
Cones

26. Experimental Results Error rate (Threshold = 1)
Cross-based
Adaptive
support-weight

27. [4]
Experimental Results
[10]
Proposed

28. After Aggregation

29. After Aggregation

30. After Aggregation

31. Conclusion

32. Conclusion Presented an efficient stereo matching algorithm
Applying a weighted median filter that is based on the proposed confidence metric.
Successfully refine initial disparities.
Competitive to the existing algorithms with a large size of aggregation kernel.