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:
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
Outline Introduction Related Work Proposed Algorithm Sparse Census Transform Generalized Census Transform Hardware Implementation Experimental Results Conclusion 2
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
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
Related Work After aggregation step: 8 Census on colors Census on gradients
Related Work Sparse census  : Half of the bits 9 X  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.
Related Work Mini-census  : 10 X  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.
Related Work Mini-census  : Mini-census adaptive support weight 11  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.
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
Transform Point Selection 19 TsukubaVenus Average TeddyCones Bright: Higher correlation accuracy Tsukuba Venus TeddyCones
Proposed Sparse Census Transform Very good correlation accuracy can be achieved using very sparse transforms. 20 16-point 12-point 8-point 4-point 2-point 1-point
Generalized Census Transform Goal : greater freedom in choosing the census transform design Definition : redrawing the transform as a graph 22
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
Hardware Implementation Pipelining : to increase throughput in an FPGA implementation 28 (Field Programmable Gate Array) Range : 0~3 3 2 1 0 One input pixel per clock cycle & Output one disparity result per clock cycle
Hardware Implementation Correlation window sum (Aggregation) : 29
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
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