1. Improved Census Transforms for Resource-Optimized Stereo Vision Wade S. Fife, Member, IEEE, James K. Archibald, Senior Member, IEEE IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 23, NO. 1, JANUARY 2013

2. Outline • Introduction • Related Work • Proposed Algorithm • Sparse Census Transform • Generalized Census Transform • Hardware Implementation • Experimental Results • Conclusion

3. Introduction

4. Introduction • The challenges: • 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

5. Objective • Propose the sparse census transforms : • Reduce the resource requirements of census-based systems • Maintain correlation accuracy • Propose the generalized census transforms : • A new class of census-like transforms • Increase the robustness and flexibility

6. Related Work

7. Related Work • CensusTransform : • Color • Gradient

8. Related Work • After aggregation step: Census on colors Census on gradients

9. Related Work • Sparse census[6] : • Half of the bits The computation costs for the hamming distances are quite large. [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.

10. Related Work • Mini-census[8] : [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. 2010.

11. Related Work • 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. 2010.

12. Related Work • Mini-census[8] :

13. ProposedAlgorithm

14. Sparse Census Transform • Definition : • N: the set of points within a T T window around p • : a new set of N

15. 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 (| | = 1) • Determine how consistently this point leads to correct correlation • 13 13 correlation window (aggregation)

16. Transform Point Selection • Go Tsukuba Venus Average Bright: Higher correlation accuracy 25 25 neighborhood Teddy Cones

18. Transform Point Selection • Further from the center : value decreasing • Very near the center : less effective • It is best to choose points that are neither too far from nor too close to the center pixel. • Optimal distance : 2 pixels • If the image is noisy should be slightly further from the center

19. Transform Point Selection With Gaussian noise ( = 5.12) Tsukuba Venus Tsukuba Venus Average Bright: Higher correlation accuracy 37 37 neighborhood Teddy Teddy Cones Cones

20. 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

21. Experimental Results

22. 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

23. 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 Horizontal + Vertical 2-edge 2-point

24. Generalized Census Transform symmetric

25. 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

26. Experimental Results

27. Experimental Results

28. Hardware Implementation • Pipelining :to increase throughput in an FPGA implementation (Field Programmable Gate Array) 3 2 1 0 3 2 1 0 3 2 1 0 3 2 1 0 3 2 1 0 One input pixel per clock cycle & Output one disparity result per clock cycle Range : 0~3

29. Hardware Implementation • Correlation window sum (Aggregation) :

30. ExperimentalResults

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

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

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

34. Experimental Results 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

35. Conclusion

36. 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