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Architectures for Stereo Vision

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Handbook of Signal Processing Systems

Abstract

Stereo vision is an elementary problem for many computer vision tasks. It has been widely studied under the two aspects of increasing the quality of the results and accelerating the computational processes. This chapter provides theoretic background on stereo vision systems and discusses architectures and implementations for real-time applications. In particular, the computationally most intensive part, the stereo matching, is discussed on the example of one of the leading algorithms, the semi-global matching (SGM). For this algorithm two implementations are presented in detail on two of the most relevant platforms for real-time image processing today: Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs). Thus, the major differences in designing parallelization techniques for extremely different image processing platforms are being illustrated.

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References

  1. Ambrosch, K., Kubinger, W.: Accurate hardware-based stereo vision. Computer Vision and Image Understanding, Elsevier 114, 1303–1316 (2010)

    Article  Google Scholar 

  2. Arias-Estrada, M., Xicotencatl, J., Brebner, G., Woods, R.: Multiple stereo matching using an extended architecture. Proc. Field-Programmable Logic and Applications 2147, 203–212 (2001)

    Article  Google Scholar 

  3. Bailey, D.G.: Design for embedded image processing on FPGAs. John Wiley & Sons, Singapore (2011)

    Book  Google Scholar 

  4. Banz, C., Blume, H., Pirsch, P.: Real-time semi-global matching disparity estimation on the GPU. Proc. IEEE Intl. Conf. Computer Vision Workshops pp. 514–521 (2011)

    Google Scholar 

  5. Banz, C., Blume, H., Pirsch, P.: Evaluation of penalty functions for SGM cost aggregation. Intl. Archives of Photogrammetry and Remote Sensing (2012)

    Google Scholar 

  6. Banz, C., Dolar, C., Cholewa, F., Blume, H.: Instruction set extension for high throughput disparity estimation in stereo image processing. Proc. IEEE Intl. Conf. Architectures and Processors Application Specific Systems pp. 169–175 (2011)

    Google Scholar 

  7. Banz, C., Hesselbarth, S., Flatt, H., Blume, H., Pirsch, P.: Real-time stereo vision system using semi-global matching disparity estimation: Architecture and FPGA-implementation. Proc. IEEE Intl. Conf. Embedded Computer Systems: Architectures, Modeling, and Simulation pp. 93–101 (2010)

    Google Scholar 

  8. Banz, C., Hesselbarth, S., Flatt, H., Blume, H., Pirsch, P.: Real-time stereo vision system using semi-global matching disparity estimation: Architecture and FPGA-implementation. Trans. High-Performance Embedded Architectures and Compilers, Springer (2012)

    Google Scholar 

  9. Birchfield, S., Tomasi, C.: A pixel dissimilarity measure that is insensitive to image sampling. IEEE Trans. Pattern Analysis and Machine Intelligence 20(4), 401–406 (1998)

    Article  Google Scholar 

  10. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. Proc. IEEE Intl. Conf. Computer Vision 1, 377–384 (1999)

    Article  Google Scholar 

  11. Bradski, G., Kaehler, A.: Learning OpenCV, 1 edn. O’Reilly, Sebastopol (2008)

    Google Scholar 

  12. Brunton, A., Chang, S., Roth, G.: Belief propagation on the GPU for stereo vision. Proc. Canadian Conf. Computer and Robot Vision p. 76 (2006)

    Google Scholar 

  13. Chang, N., Lin, T.M., Tasi, T.H., Tseng, Y.C., Chang, T.S.: Real-time DSP implementation on local stereo matching. Proc. IEEE Intl. Conf. Multimedia and Expo pp. 2090–2093 (2007)

    Google Scholar 

  14. Chang, N., Tasi, T.H., Hsu, B., Chen, Y., Chang, T.S.: Algorithm and architecture of disparity estimation with mini-census adaptive support weight. IEEE Trans. Circuits and Systems for Video Technology 20(6), 792–805 (2010)

    Article  Google Scholar 

  15. Chu, P.P.: RTL hardware design using VHDL: Coding for efficiency, portability, and scalability. Wiley-Interscience, Hoboken and N.J (2006)

    Book  Google Scholar 

  16. Cornells, N., van Gool, L.: Real-time connectivity constrained depth map computation using programmable graphics hardware. Proc. IEEE Conf. Computer Vision and Pattern Recognition 1, 1099–1104 (2005)

    Google Scholar 

  17. Darabiha, A., MacLean, W., Rose, J.: Reconfigurable hardware implementation of a phase-correlation stereo algorithm. Machine Vision and Applications, Springer 17, 116–132 (2006)

    Article  Google Scholar 

  18. Diaz, J., Ros, E., Carrillo, R., Prieto, A.: Real-time system for high-image resolution disparity estimation. IEEE Trans. Image Processing 16(1), 280–285 (2007)

    Article  MathSciNet  Google Scholar 

  19. Ernst, I., Hirschmüller, H.: Mutual information based semi-global stereo matching on the GPU. Proc. Intl. Symp. Visual Computing 5358, 228–239 (2008)

    Article  Google Scholar 

  20. Ess, A., Leibe, B., Schindler, K., van Gool, L.: A mobile vision system for robust multi-person tracking. Proc. IEEE Conf. Computer Vision and Pattern Recognition pp. 1–8 (2008)

    Google Scholar 

  21. Faugeras, O., Viéville, T., Theron, E., Vuillemin, J., Hotz, B., Zhang, Z., Moll, L., Bertin, P., Mathieu, H., Fua, P., Berry, G., Proy, C.: Real-time correlation-based stereo: Algorithm, implementations and applications (1993)

    Google Scholar 

  22. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient belief propagation for early vision. Intl. Journal of Computer Vision, Springer 70, 41–54 (2006)

    Google Scholar 

  23. Forstmann, S., Kanou, Y., Jun, O., Thuering, S., Schmitt, A.: Real-time stereo by using dynamic programming. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop p. 29 (2004)

    Google Scholar 

  24. Gehrig, S., Rabe, C.: Real-time semi-global matching on the CPU. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop pp. 85–92 (2010)

    Google Scholar 

  25. Gehrig, S.K., Eberli, F., Meyer, T.: A real-time low-power stereo vision engine using semi-global matching. Proc. Intl. Conf. Computer Vision Systems 5815, 134–143 (2009)

    Article  Google Scholar 

  26. Georgoulas, C., Andreadis, I.: A real-time fuzzy hardware structure for disparity map computation. Journal of Real-Time Image Processing, Springer 6(4), 257–273 (2011)

    Article  Google Scholar 

  27. Gibson, J., Marques, O.: Stereo depth with a unified architecture GPU. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop pp. 1–6 (2008)

    Google Scholar 

  28. Gong, M.: Real-time joint disparity and disparity flow estimation on programmable graphics hardware. Computer Vision and Image Understanding, Elsevier 113(1), 90–100 (2009)

    Article  Google Scholar 

  29. Gong, M., Yang, R., Wang, L., Gong Mingwei: A performance study on different cost aggregation approaches used in real-time stereo matching. Intl. Journal of Computer Vision, Springer 75, 283–296 (2007)

    Google Scholar 

  30. Gong, M., Yang, Y.H.: Near real-time reliable stereo matching using programmable graphics hardware. Proc. IEEE Conf. Computer Vision and Pattern Recognition 1, 924–931 (2005)

    Google Scholar 

  31. Haller, I., Nedevschi, S.: GPU optimization of the SGM stereo algorithm. Proc. IEEE Intl. Conf. Intelligent Computer Communication and Processing pp. 197–202 (2010)

    Google Scholar 

  32. Haller, I., Nedevschi, S.: Design of interpolation functions for subpixel-accuracy stereo-vision systems. IEEE Trans. Image Processing 21(2), 889–898 (2012)

    Article  MathSciNet  Google Scholar 

  33. Harris: Optimizing parallel reduction in CUDA (2007). Whitepaper included in Nvidia Cuda SDK 4.0

    Google Scholar 

  34. Hartley, R.I., Zisserman, A.: Multiple view geometry in computer vision, 2. ed., 7. print. edn. Cambridge Univ. Press, Cambridge (2010)

    Google Scholar 

  35. Hennessy, J.L., Patterson, D.A.: Computer architecture: A quantitative approach, 5 edn. Morgan Kaufmann, San Francisco and Calif and Oxford (2011)

    Google Scholar 

  36. Hirschmüller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. Proc. IEEE Conf. Computer Vision and Pattern Recognition 2, 807–814 (2005)

    Google Scholar 

  37. Hirschmüller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Analysis and Machine Intelligence 30(2), 328–341 (2008)

    Article  Google Scholar 

  38. Hirschmüller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. Proc. IEEE Conf. Computer Vision and Pattern Recognition pp. 1–8 (2007)

    Google Scholar 

  39. Hirschmüller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. IEEE Trans. Pattern Analysis and Machine Intelligence 31(9), 1582–1599 (2009)

    Article  Google Scholar 

  40. Hosni, A., Bleyer, M., Rhemann, C., Gelautz, M., Rother, C.: Real-time local stereo matching using guided image filtering. Proc. IEEE Intl. Conf. Multimedia and Expo pp. 1–6 (2011)

    Google Scholar 

  41. Hosseini, F., Fijany, A., Safari, S., Fontaine, J.: Fast implementation of dense stereo vision algorithms on a highly parallel SIMD architecture. Journal of Real-Time Image Processing, Springer pp. 1–15 (2011)

    Google Scholar 

  42. Humenberger, M., Zinner, C., Kubinger, W.: Performance evaluation of a census-based stereo matching algorithm on embedded and multi-core hardware. Proc. Intl. Symp. Image and Signal Processing and Analysis pp. 388–393 (2009)

    Google Scholar 

  43. Ivanchenko, V., Shen, H., Coughlan, J.: Elevation-based MRF stereo implemented in real-time on a GPU. Workshop Applications of Computer Vision pp. 1–8 (2009)

    Google Scholar 

  44. Jiangbo, L., Rogmans, S., Lafruit, G., Catthoor, F.: Real-time stereo correspondence using a truncated separable laplacian kernel approximation on graphics hardware. Proc. IEEE Intl. Conf. Multimedia and Expo pp. 1946–1949 (2007)

    Google Scholar 

  45. Jiangbo, L., Zhang, K., Lafruit, G., Catthoor, F.: Real-time stereo matching: a cross-based local approach. Proc. IEEE Intl. Conf. Acoustics, Speech and Signal Processing pp. 733–736 (2009)

    Google Scholar 

  46. Jin, S., Cho, J., Pham, X.D., Lee, K.M., Park, S.K., Kim, M., Jeon, J.W.: FPGA design and implementation of a real-time stereo vision system. IEEE Trans. Circuits and Systems for Video Technology 20(1), 15–26 (2010)

    Article  Google Scholar 

  47. Kalarot, R., Morris, J.: Comparison of FPGA and GPU implementations of real-time stereo vision. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop pp. 9–15 (2010)

    Google Scholar 

  48. Kalarot, R., Morris, J., Gimel’farb, G.: Performance analysis of multi-resolution symmetric dynamic programming stereo on GPU. Proc. Intl. Conf. Image and Vision Computing New Zealand pp. 1–7 (2010)

    Google Scholar 

  49. Kanade, T., Yoshida, A., Oda, K., Kano, H., Tanaka, M.: A stereo machine for video-rate dense depth mapping and its new applications. Proc. IEEE Conf. Computer Vision and Pattern Recognition pp. 196–202 (1996)

    Google Scholar 

  50. Ke, Z., Jiangbo, L., Qiong, Y., Lafruit, G., Lauwereins, R., van Gool, L.: Real-Time and Accurate Stereo: A Scalable Approach With Bitwise Fast Voting on CUDA. IEEE Trans. Circuits and Systems for Video Technology 21(7), 867–878 (2011)

    Article  Google Scholar 

  51. Kim, J., Kolmogorov, V., Zabih, R.: Visual correspondence using energy minimization and mutual information. Proc. IEEE Intl. Conf. Computer Vision pp. 1033–1040 (2003)

    Google Scholar 

  52. Konolige, K.: Small vision systems: Hardware and implementation. Proc. Intl. Symp. Robotic Research (1997)

    Google Scholar 

  53. Kung, M., Au, O., Wong, P., Chun, H.L.: Block based parallel motion estimation using programmable graphics hardware. Proc. Intl. Conf. Audio, Language and Image Processing pp. 599–603 (2008)

    Google Scholar 

  54. Lee, S.H., Yi, J., Kim, J.S.: Real-time stereo vision on a reconfigurable system. Proc. Intl. Conf. Embedded Computer Systems: Architectures, Modeling, and Simulation Workshops 3553, 299–307 (2005)

    Google Scholar 

  55. Liang, C., Cheng, C., Lai, Y., Chen, L., Chen, H.: Hardware-efficient belief propagation. IEEE Trans. Circuits and Systems for Video Technology 21(5), 525–537 (2011)

    Article  Google Scholar 

  56. Liang, W., Miao, L., Minglun, G., Ruigang, Y., Nister, D.: High-quality real-time stereo using adaptive cost aggregation and dynamic programming. Proc. Intl. Symp. 3D Data Processing, Visualization, and Transmission pp. 798–805 (2006)

    Google Scholar 

  57. Liang, W., Mingwei, G., Minglun, G., Ruigang, Y.: How far can we go with local optimization in real-time stereo matching. Proc. Intl. Symp. 3D Data Processing, Visualization, and Transmission pp. 129–136 (2006)

    Google Scholar 

  58. Liu, J., Xu, Y., Klette, R., Chen, H., Vaudrey, T.: Disparity Map Computation on a Cell Processor (2009)

    Google Scholar 

  59. van der Mark, W., Gavrila, D.: Real-time dense stereo for intelligent vehicles. IEEE Trans. Intelligent Transportation Systems 7(1), 38–50 (2006)

    Article  Google Scholar 

  60. Masrani, D., MacLean, W.: A real-time large disparity range stereo-system using FPGAs. Proc. Intl. Conf. Computer Vision Systems p. 13 (2006)

    Google Scholar 

  61. Mattoccia, S., Viti, M., Ries, F.: Near real-time Fast Bilateral Stereo on the GPU. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop pp. 136–143 (2011)

    Google Scholar 

  62. Mei, X., Sun, X., Zhou, M., Jiao, S., Wang, H., Zhang, X.: On building an accurate stereo matching system on graphics hardware. Proc. IEEE Intl. Conf. Computer Vision Workshops pp. 467–474 (2011)

    Google Scholar 

  63. Minglun, G., Ruigang, Y.: Image-gradient-guided real-time stereo on graphics hardware. Proc. Intl Conf. 3D Digital Imaging and Modeling pp. 548–555 (2005)

    Google Scholar 

  64. Miyajima, Y., Maruyama, T.: A real-time stereo vision system with FPGA. Proc. Intl. Conf. Field Programmable Logic And Application 2778, 448–457 (2003)

    Article  Google Scholar 

  65. Morris, J., Jawed, K., Gimel’farb, G., Khan, T.: Breaking the ‘Ton’: Achieving 1% depth accuracy from stereo in real time. Proc. Intl. Conf. Image and Vision Computing New Zealand pp. 142–147 (2009)

    Google Scholar 

  66. Mühlmann, K., Maier, D., Hesser, J., Manner, R.: Calculating dense disparity maps from color stereo images, an efficient implementation. Intl. Journal of Computer Vision, Springer 47(1–3), 79–88 (2002)

    Google Scholar 

  67. Pantilie, C., Nedevschi, S.: SORT-SGM: Subpixel optimized real-time semiglobal matching for intelligent vehicles. IEEE Trans. Vehicular Technology 61(3), 1032–1042 (2012)

    Article  Google Scholar 

  68. Park, S., Jeong, H.: Real-time stereo vision FPGA chip with low error rate. Proc. Intl. Conf. Multimedia and Ubiquitous Engineering pp. 751–756 (2007)

    Google Scholar 

  69. Paya Vaya, G., Martin Langerwerf, J., Banz, C., Giesemann, F., Pirsch, P., Blume, H.: VLIW architecture optimization for an efficient computation of stereoscopic video applications. Proc. Intl. Conf. Green Circuits and Systems pp. 457–462 (2010)

    Google Scholar 

  70. Paya-Vaya, G., Martin-Langerwerf, J., Pirsch, P.: A multi-shared register file structure for VLIW processors. Journal of Signal Processing Systems, Springer 58(2), 215–231 (2010)

    Article  Google Scholar 

  71. Perez, J., Sanchez, P., Martinez, M.: High memory throughput FPGA architecture for high-definition Belief-Propagation stereo matching. Proc. Intl. Conf. Signals, Circuits and Systems pp. 1–6 (2009)

    Google Scholar 

  72. Pirsch, P.: Architectures for digital signal processing. John Wiley & Sons, Inc., Chichester (2008)

    Google Scholar 

  73. Pock, T., Schoenemann, T., Graber, G., Bischof, H., Cremers, D.: A convex formulation of continuous multi-label problems. Proc. European Conference on Computer Vision 5304, 792–805 (2008)

    Google Scholar 

  74. Podlozhnyuk, V.: Image Convolution with CUDA (2007). Whitepaper included in Nvidia Cuda SDK 4.0

    Google Scholar 

  75. Ranft, B., Schoenwald, T., Kitt, B.: Parallel matching-based estimation - a case study on three different hardware architectures. Proc. IEEE Intelligent Vehicles Symposium pp. 1060–1067 (2011)

    Google Scholar 

  76. Ruigang, Y., Pollefeys, M.: Multi-resolution real-time stereo on commodity graphics hardware. Proc. IEEE Conf. Computer Vision and Pattern Recognition 1, I–211–I–217 (2003)

    Google Scholar 

  77. Ruigang, Y., Welch, G., Bishop, G.: Real-time consensus-based scene reconstruction using commodity graphics hardware. Proc. Pacific Conf. Computer Graphics and Applications pp. 225–234 (2002)

    Google Scholar 

  78. Sabihuddin, S., Islam, J., MacLean, W.: Dynamic programming approach to high frame-rate stereo correspondence: A pipelined architecture implemented on a field programmable gate array. Proc. Canadian Conf. Electrical and Computer Engineering pp. 001,461–001,466 (2008)

    Google Scholar 

  79. Safari, S., Fijany, A., Diotalevi, F., Hosseini, F.: Highly parallel and fast implementation of stereo vision algorithms on MIMD many-core Tilera architecture. Proc. IEEE Aerospace Conference pp. 1–11 (2012)

    Google Scholar 

  80. Scharstein, D., Szeliski, R.: The Middlebury Stereo Pages. http://vision.middlebury.edu/stereo/

  81. Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Intl. Journal of Computer Vision, Springer 47(1), 7–42 (2002)

    Google Scholar 

  82. Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. Proc. IEEE Conf. Computer Vision and Pattern Recognition 1, I–195–I–202 (2003)

    Google Scholar 

  83. Szeliski, R.: Computer vision: Algorithms and applications. Springer, London and New York (2011)

    MATH  Google Scholar 

  84. Szeliski, R., Scharstein, D.: Sampling the disparity space image. IEEE Trans. Pattern Analysis and Machine Intelligence 26(3), 419–425 (2004)

    Article  Google Scholar 

  85. Tomasi, M., Vanegas, M., Barranco, F., Daz, J., Ros, E.: Massive parallel-hardware architecture for multiscale stereo, optical flow and image-structure computation. IEEE Trans. Circuits and Systems for Video Technology 22(2), 282–294 (2012)

    Article  Google Scholar 

  86. Tombari, F., Mattoccia, S., Di Stefano, L., Addimanda, E.: Classification and evaluation of cost aggregation methods for stereo correspondence. Proc. IEEE Conf. Computer Vision and Pattern Recognition pp. 1–8 (2008)

    Google Scholar 

  87. Tseng, Y.C., Chang, T.S.: Architecture design of belief propagation for real-time disparity estimation. IEEE Trans. Circuits and Systems for Video Technology 20(11), 1555–1564 (2010)

    Article  MathSciNet  Google Scholar 

  88. Ttofis, C., Hadjitheophanous, S., Georghiades, A., Theocharides, T.: Edge-directed hardware architecture for real-time disparity map computation. IEEE Trans. Computers PP(99), 1 (2012)

    Google Scholar 

  89. Ttofis, C., Theocharides, T.: Towards accurate hardware stereo correspondence: A real-time FPGA implementation of a segmentation-based adaptive support weight algorithm. Proc. Conf. Design, Automation & Test in Europe pp. 703–708 (2012)

    Google Scholar 

  90. Vaish, V., Levoy, M., Szeliski, R., Zitnick, C., Sing, B.K.: Reconstructing occluded surfaces using synthetic apertures: Stereo, focus and robust measures. Proc. IEEE Conf. Computer Vision and Pattern Recognition 2, 2331–2338 (2006)

    Google Scholar 

  91. Villalpando, C., Morfopolous, A., Matthies, L., Goldberg, S.: FPGA implementation of stereo disparity with high throughput for mobility applications. Proc. IEEE Aerospace Conference pp. 1–10 (2011)

    Google Scholar 

  92. Weber, M., Humenberger, M., Kubinger, W.: A very fast census-based stereo matching implementation on a graphics processing unit. Proc. IEEE Intl. Conf. Computer Vision Workshops pp. 786–793 (2009)

    Google Scholar 

  93. Woodfill, J., Gordon, G., Buck, R.: Tyzx DeepSea high speed stereo vision system. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop p. 41 (2004)

    Google Scholar 

  94. Woodfill, J., Gordon, G., Jurasek, D., Brown, T., Buck, R.: The Tyzx DeepSea G2 vision system, a taskable, embedded stereo camera. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop p. 126 (2006)

    Google Scholar 

  95. Woodfill, J., Herzen, B.v.: Real-time stereo vision on the PARTS reconfigurable computer. Proc. IEEE Symp. FPGAs for Custom Computing Machines pp. 201–210 (1997)

    Google Scholar 

  96. Xu, Y., Chen, H., Klette, R., Liu, J., Vaudrey, T.: Belief propagation implementation using CUDA on an Nvidia GTX 280. Proc. Advances in Artificial Intelligence 5866, 180–189 (2009)

    Google Scholar 

  97. Yang, Q., Wang, L., Yang, R., Wang, S., Liao, M., Nister, D.: Real-time global stereo matching using hierarchical belief propagation. Proc. The British Machine Vision Conference pp. 989–998 (2006)

    Google Scholar 

  98. Yunde, J., Xiaoxun, Z., Mingxiang, L., Luping: A miniature stereo vision machine (MSVM-III) for dense disparity mapping. Proc. IEEE Intl. Conf. Pattern Recognition 1, 728–731 (2004)

    Google Scholar 

  99. Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. Proc. European Conference on Computer Vision pp. 151–158 (1994)

    Google Scholar 

  100. Zach, C., Klaus, A., Hadwiger, M., Karner, K.: Accurate dense stereo reconstruction using graphics hardware. Proc. EUROGRAPHICS pp. 227–234 (2003)

    Google Scholar 

  101. Zach, C., Sormann, M., Karner, K.: Scanline optimization for stereo on graphics hardware. Proc. Intl. Symp. 3D Data Processing, Visualization, and Transmission pp. 512–518 (2006)

    Google Scholar 

  102. Zatt, B., Shafique, M., Bampi, S., Henkel, J.: Multi-level pipelined parallel hardware architecture for high throughput motion and disparity estimation in Multiview Video Coding. Proc. Conf. Design, Automation & Test in Europe pp. 1–6 (2011)

    Google Scholar 

  103. Zhang, Z.: Determining the epipolar geometry and its uncertainty: A review. Intl. Journal of Computer Vision, Springer 27(2), 161–195 (1998)

    Google Scholar 

  104. Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)

    Article  Google Scholar 

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  1. Institute of Microelectronic Systems, Leibniz University of Hannover Appelstr. 4, 30167, Hannover, Germany

    Christian Banz, Holger Blume & Peter Pirsch

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  1. Christian Banz
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  2. Holger Blume
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  3. Peter Pirsch
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Correspondence to Christian Banz .

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  1. , Dept. of Electrical and, University of Maryland, A. V. Williams Bldg. 2311, College Park, 20742, Maryland, USA

    Shuvra S. Bhattacharyya

  2. Leiden Inst. Advanced Computer Science, Leiden Embedded Research Center, Leiden University, Niels Bohrweg 1, Leiden, 2333 CA, Netherlands

    Ed F. Deprettere

  3. Software for Systems on Silicon, RWTH Aachen University, Templergraben 55, Aachen, 52056, Germany

    Rainer Leupers

  4. , Department of Pervasive Computing, Tampere University of Technology, Korkeakoulunkatu 1, Tampere, 33720, Finland

    Jarmo Takala

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Banz, C., Blume, H., Pirsch, P. (2013). Architectures for Stereo Vision. In: Bhattacharyya, S., Deprettere, E., Leupers, R., Takala, J. (eds) Handbook of Signal Processing Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6859-2_16

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