1. Improved Census Transforms for Resource-Optimized Stereo Vision
Speaker ： Kai-Wen, Weng
Author： Wade S. Fife, James K. Archibald
Good afternoon, ladies and gentlemen.
I’m Kai-Wen, Weng.
Today, I’m going to give a talk on the
Improved Census Transforms for Resource-Optimized Stereo Vision.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 23, NO. 1, JANUARY 2013

2. Outline Introduction Proposed Algorithm Experimental Results
Sparse Census Transform
Generalized Census Transform
Experimental Results
Conclusion
First. I'll introduce the census transform of this paper.
Next I’ll explain proposed algorithm on this paper
At last, I’d like to highlight some of the experimental results and conclusions from this study.
//// Next I’ll go on to outline the related work in this study.

3. Introduction

4. Introduction Computation costs algorithm：
SAD (Sum of Absolute Differences)
SSD (Sum of Squared Differences)
NCC (Normalized Cross Correlation)
CT (Census Transform)
Reference
Target
Disparity

5. Introduction
Census Transform :
Color
Gradient

6. Introduction After aggregation step: Census on colors
Census on gradients

7. Introduction Sparse census[6] : Half of the bits
The computation costs for the hamming distances are quite large. X
[6] C. Zinner, M. Humenberger, K. Ambrosch, and W. Kubinger, “An optimized software-based implementation of a census-based stereo matching algorithm,” in Proc. 4th ISVC, 2008, pp. 216–227.

8. Introduction Mini-census[8] : X
[8] N.-C. Chang, T.-H. Tsai, B.-H. Hsu, Y.-C. Chen, and T.-S. Chang,“Algorithm and architecture of disparity estimation with mini-census adaptive support weight,” IEEE Trans. Circuits Syst. Video Technol., vol. 20, no. 6, pp. 792–805, Jun

9. Introduction Mini-census[8] : Mini-census adaptive support weight
[8] N.-C. Chang, T.-H. Tsai, B.-H. Hsu, Y.-C. Chen, and T.-S. Chang,“Algorithm and architecture of disparity estimation with mini-census adaptive support weight,” IEEE Trans. Circuits Syst. Video Technol., vol. 20, no. 6, pp. 792–805, Jun

10. Introduction The challenges: It is critical to…
The enormous amount of computation required to identify the corresponding points in the images.
It is critical to…
maximize the accuracy and throughput of the stereo system
while minimizing the resource requirements

11. Proposed Algorithm

12. Transform Point Selection
Goal : minimize the size of the census transform vector
Challenge: Must quantify how much each point in the transform window contributes to overall correlation accuracy
Test correlation accuracy:
Define a sparse census transform consisting of a single point (| 𝑵 | = 1)
Determine how consistently this point leads to correct correlation
13 × 13 correlation window (aggregation)

13. Tsukuba
Venus
Teddy
Cones

14. Transform Point SelectionGo
Tsukuba
Venus
Average
Bright:
Higher correlation accuracy
25 × 25 neighborhood
Teddy
Cones

15. Proposed Sparse Census Transform
Very good correlation accuracy can be achieved using very sparse transforms.
16-point
12-point
8-point
4-point
2-point
1-point

16. Experimental Results

17. Generalized Census Transform
Goal : greater freedom in choosing the census transform design
Definition : redrawing the transform as a graph
3 × 3 correlation
(aggregation)
3 × 3 census

18. Generalized Census Transform
As..
(1)transform neighborhoods become more and more sparse
(2)fewer pixels are used in the correlation process
selection of points to include in the transform becomes more critical
如..（1）轉換的鄰點變得越來越稀疏（2）較少的像素中的相關處理中使用
選點在變換變得更重要，以包括
Horizontal + Vertical
2-edge
2-point

19. Generalized Census Transform
symmetric

20. Proposed Generalized Census Transform
Benefits :
Often require a smaller census transform window (memory)
Increased robustness under varying conditions (noise)
16-edge
12-edge
8-edge
4-edge
2-edge
1-edge

21. Experimental Results

22. Experimental Results

23. Experimental Results

24. Left Image
Ground Truth
Full 7x7 census
12-edge
4-edge

25. Left Image
Ground Truth
Full 7x7 census
12-edge
4-edge

26. Left Image
Full 7x7 census
12-edge
4-edge

27. Experimental Results
𝟖𝟖%↓
𝟔𝟏%↓
LUTs (look-up tables) : the amount of logic required to implement the method
FFs : the number of 1-bit registers (the amount of pipelining used)
RAMs : the number of 18-kbit block memories
Freq. : the maximum operating frequency reported by synthesis

28. Conclusion

29. Conclusion Proposed and analyzed in this paper:
A range of sparse census transforms
reduce hardware resource requirements
attempting to maximize correlation accuracy.
often better than or nearly as good as the full census
Generalized census transforms
increased robustness in the presence of image noise
提出並在此進行了分析：
稀疏CT的範圍
減少硬體資源要求
試圖最大限度的相關精度。
比Full CT 轉換來的好
一般化的CT
在圖像noise的情況下提高了穩定性，

30. Q&A