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基于FPGA的立体视觉匹配的高性能实现 被引量:6

High Performance Implementation of Stereo Vision Matching Based on FPGA
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摘要 立体视觉系统在3维场景信息感知中起着重要的作用。其中立体匹配算法的运算复杂度较高,实时处理需要硬件实现匹配运算。但在现有的不多实现中,性能要求和硬件资源的矛盾突出。随着分辨率的增加,对处理速度和视差搜索范围都有更高的要求。对此,该文提出了一种立体匹配硬件实现结构,通过并行化算法子模块和合理安排流水结构来提高性能。匹配算法引入了自适应相关窗口的匹配策略,提升了深度不连续区域的视差质量。该方法结合左右一致性校验准则,可有效去除大部分错误匹配结果。整个匹配流程在单片现场可编程门阵列(FPGA)上实现,并在有限硬件资源条件下将视差搜索范围扩大到128像素。系统时钟60 MHz时,对于512×512分辨率的立体图像,系统可以实现60帧/秒以上的处理速度。 Stereo vision system plays important role in three-dimensional information perception.Due to the high computational complexity,real-time processing of stereo vision needs to use dedicated hardware.However,performance requirements conflict with hardware resources in existing implementations.With the resolution increased,system requires larger disparity range and higher processing speed.In this paper,a stereo vision implementation is proposed using fine-grain pipelined structure and sub-module parallelism to improve performance.The implemented matching algorithm used adaptive correlation window strategy to raise disparity quality at object borders and integrated left-right consistency check to reduce possible errors in general.The entire stereo matching process is realized using a single chip of Field Programmable Gate Array(FPGA) and extended disparity search range to 128 pixels under limited resources.The matching process is capable of generating disparities at more than 60 frames per second on 512×512 images when clocked at 60 MHz.
出处 《电子与信息学报》 EI CSCD 北大核心 2011年第3期597-603,共7页 Journal of Electronics & Information Technology
基金 国家自然科学基金(60534070 90820306)资助课题
关键词 图像处理 立体视觉 实时性 FPGA 自适应相关窗口 Image processing Stereo vision Real-time FPGA Adaptive correlation window
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参考文献11

  • 1Forstman S, Kanou Y, and Ohua J, et al.. Real-time stereo by using dynamic programming [C]. Proceedings of the Conference on Computer Vision and Pattern Recognition, Washington, DC, Jul.02, 2004, Vol.3: 29. 被引量:1
  • 2Gong M and Yang Y. Near real-time reliable stereo matching using programmable graphic hardware [C]. Proceedings of the Conference on Computer Vision and Pattern Recognition San Diego, Jun.20, 2005, Vol.l: 924-931. 被引量:1
  • 3Wei Yu, Tsuhan C, and Hoe J C. Real time stereo vision using exponential step cost aggregation on GPU [C]. Proceedings of the International Conference on Image Processing, Cairo, Nov. 2009: 4281-4284. 被引量:1
  • 4Woodfill J and Von-Herzen B. Real-time stereo vision on the PARTS reconfigurable computer[C]. Proceedings of 5th IEEE Symposium on FPGAs for Custom Computing Machines, Napa California, Apr. 16, 1997: 201-210. 被引量:1
  • 5Ambrosch K, Humenberger M, and Kubinger W, et aL. Hardware implementation of an SAD based stereo vision algorithm [C]. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops, Minneapolis, Jun. 17, 2007: 1-6. 被引量:1
  • 6Chen L and Jia Yunde. A Parallel reconfigurable architecture for reM-time stereo vision [C]. Proceedings of the International Conference on Embedded Software and Systems, Hangzhou, May 25, 2009: 32-39. 被引量:1
  • 7Jin S, Cho J, and Phan X, et al.. FPGA design and implementation of a Real-Time stereo vision system[J]. IEEETransaction on Circuits and Systems for Video Technology, 2010, 20(1): 15-26. 被引量:1
  • 8Zabin R and Woodfill J. Non-parametric local transforms for computing visual correspondence [C]. Proceedings of 3rd European Conf. Computer Vision, Stockholm, May 1994: 150-158. 被引量:1
  • 9Hirschmtlller H, Innocent P R, and Garibaldi J. Real-time correlation-based stereo vision with reduced border errors [J]. International Journal of Computer Vision, 2004, 47(1-3): 229-246. 被引量:1
  • 10Mtthlmann K, Maiser D, and Hesser J, et al.. Calculating dense disparity maps from color stereo images, an efficient implementation [J]. International Journal of Computer Vision, 2004, 47(1): 79-88. 被引量:1

同被引文献65

  • 1崔岩,蔡炳煌,李大勇,胡宏勋,董静微.太阳能光伏系统MPPT控制算法的对比研究[J].太阳能学报,2006,27(6):535-539. 被引量:177
  • 2Scharstein D and Szeliski R.A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[J].International Journal of Computer Vision,2002,47(1-3):7-42. 被引量:1
  • 3Yoon K J and Kweon S.Adaptive support-weight approach for correspondence search[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):650-656. 被引量:1
  • 4De-Maeztu L,Villanueva A,and Cabeza R.Stereo matching using gradient similarity and locally adaptive support-weight[J].Pattern Recognition Letters,2011,32(13):1643-1651. 被引量:1
  • 5Heo Y S,Lee K M,and Lee S U.Robust stereo matching using adaptive normalized cross-correlation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(4):807-822. 被引量:1
  • 6Li Li,Zhang Cai-ming,and Yan Hua.Cost aggregation strategy for stereo matching based on a generalized bilateral filter model[C].Proceedings of International Conference on Information Computing and Applications,Tangshan,China,2010:193-200. 被引量:1
  • 7Richard C,Orr D,and Davies I,et al..Real-time spatiotemporal stereo matching using the dual-cross-bilateral grid[C].Proceedings of the11th European Conference on Computer Vision Conference on Computer Vision,Crete,Greece,2010:510-523. 被引量:1
  • 8Bobick A F and Intille S S.Large occlusions stereo[J].International Journal of Computer Vision,1999,33(3):181-200. 被引量:1
  • 9Sun Jian,Zheng Nan-ning,and Shum H Y.Stereo matching using belief propagation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(7):787-800. 被引量:1
  • 10Papadakis N and Caselles V.Multi-label depth estimation for graph cuts stereo problems[J].Journal of Mathematical Imaging and Vision,2010,38(1):70-82. 被引量:1

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