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Improved Census Transforms for Resource-Optimized Stereo Vision Wade S. Fife, Member, IEEE, James K. Archibald, Senior Member, IEEE IEEE TRANSACTIONS ON.

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Presentation on theme: "Improved Census Transforms for Resource-Optimized Stereo Vision Wade S. Fife, Member, IEEE, James K. Archibald, Senior Member, IEEE IEEE TRANSACTIONS ON."— Presentation transcript:

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 2

3 Introduction 3

4 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 4

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 5

6 Related Work 6

7 Census Transform : Color Gradient 7

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

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

10 Related Work Mini-census [8] : 10 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

11 Related Work Mini-census [8] : Mini-census adaptive support weight 11 [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

12 Related Work Mini-census [8] : 12

13 Proposed Algorithm 13

14 Sparse Census Transform 14 P’ P

15 Transform Point Selection 15

16 Transform Point Selection Go 16 TsukubaVenus Average TeddyCones Bright: Higher correlation accuracy

17

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 18 from the center

19 Transform Point Selection 19 TsukubaVenus Average TeddyCones Bright: Higher correlation accuracy Tsukuba Venus TeddyCones

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

21 Experimental Results 21

22 Generalized Census Transform Goal : greater freedom in choosing the census transform design Definition : redrawing the transform as a graph 22

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 23 2-point 2-edge Horizontal + Vertical

24 Generalized Census Transform 24 symmetric

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

26 Experimental Results 26

27 Experimental Results 27

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

29 Hardware Implementation Correlation window sum (Aggregation) : 29

30 Experimental Results 30

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

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

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

34 Experimental Results 34 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 35

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 36


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