caa a22 4500 467937001 CHVBK 20180406153000.0 cr unu---uuuuu 170328e20060501xx s 000 0 eng 10.1007/s00138-006-0018-2 doi (NATIONALLICENCE)springer-10.1007/s00138-006-0018-2 Reconfigurable hardware implementation of a phase-correlation stereoalgorithm [Elektronische Daten] [Ahmad Darabiha, W. MacLean, Jonathan Rose] This paper describes the implementation of a stereo-vision system using Field Programmable Gate Arrays (FPGAs). Reconfigurable hardware, including FPGAs, is an attractive platform for implementing vision algorithms due to its ability to exploit parallelism often found in these algorithms, and due to the speed with which applications can be developed as compared to hardware. The system outputs 8-bit, subpixel disparity estimates for 256× 360 pixel images at 30,fps. A local-weighted phase correlation algorithm for stereo disparity [Fleet, D. J.: {Int. Conf. Syst. Man Cybernetics 1:48-54 (1994)] is implemented. Despite the complexity of performing correlations on multiscale, multiorientation phase data, the system runs as much as 300 times faster in hardware than its software implementation. This paper describes the hardware platform used, the algorithm, and the issues encountered during its hardware implementation. Of particular interest is the implementation of multiscale, steerable filters, which are widely used in computer vision algorithms. Several trade-offs (reducing the number of filter orientations from three to two, using fixed-point computation, changing the location of one localized low-pass filter, and using L1 instead of L2 norms) were required to both fit the design into the available hardware and to achieve video-rate processing. Finally, results from the system are given both for synthetic data sets as well as several standard stereo-pair test images. Springer-Verlag, 2006 Stereo disparity estimation nationallicence Frame rate implementation nationallicence Field Programmable Gate Arrays (FPGAs) nationallicence Reconfigurable hardware implementation nationallicence Phase correlation nationallicence Darabiha Ahmad Department of Electrical and Computer Engineering, University of Toronto, M5S 1A1, Toronto, Canada aut MacLean W. Department of Electrical and Computer Engineering, University of Toronto, M5S 1A1, Toronto, Canada aut Rose Jonathan Department of Electrical and Computer Engineering, University of Toronto, M5S 1A1, Toronto, Canada aut Machine Vision and Applications Springer-Verlag 17/2(2006-05-01), 116-132 0932-8092 17:2<116 2006 17 138 https://doi.org/10.1007/s00138-006-0018-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00138-006-0018-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Darabiha Ahmad Department of Electrical and Computer Engineering, University of Toronto, M5S 1A1, Toronto, Canada aut NATIONALLICENCE 700 1- MacLean W. Department of Electrical and Computer Engineering, University of Toronto, M5S 1A1, Toronto, Canada aut NATIONALLICENCE 700 1- Rose Jonathan Department of Electrical and Computer Engineering, University of Toronto, M5S 1A1, Toronto, Canada aut NATIONALLICENCE 773 0- Machine Vision and Applications Springer-Verlag 17/2(2006-05-01), 116-132 0932-8092 17:2<116 2006 17 138 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer