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基于递推自适应权重的快速稠密立体匹配

, PP. 963-967

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Abstract:

针对经典自适应权重稠密立体匹配算法计算量大的问题,提出了一种递推自适应权重算法.重新定义相邻像素的权重为距离衰减因子和色彩差异函数的乘积,不相邻像素权重为相邻像素权重的累乘,色彩差异越小、距离越近的像素权重越大;证明了在新的权重定义下,一维空间的匹配代价融合可以通过两次递推完成,真实图像的匹配代价融合可以通过4次递推完成,同时给出相应递推公式;递推匹配代价融合时每个像素每一视差只做4次乘法和8次加法,计算量比窗口大小为35×35的经典自适应权重算法小约两个数量级;基于递推匹配代价融合实现了一种快速稠密立体匹配算法.使用Middlebury大学的测评集测试该算法,证明了递推自适应权重算法的速度和精度均优于经典自适应权重算法.

References

[1]  Scharstein D,Szeliski R.A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[J].International Journal of Computer Vision,2002,47(1):7-42
[2]  张广军.视觉测量[M].北京:科学出版社,2008:148-158 Zhang Guangjun.Vision measurement[M].Beijing:Science Press,2008:148-158(in Chinese)
[3]  Yoon K J,Kweon I S.Adaptive support-weight approach for correspondence search[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):650-656
[4]  Klaus A,Sormann M,Karner K.Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure //International Conference on Pattern Recognition.Hong-kong:International Association for Pattern Recognition,2006:15-18
[5]  Scharstein D,Szeliski R.Middlebury stereo vision .(2012-04-05) .http://vision.middlebury.edu/stereo/
[6]  Dongbo M,Jiangbo L,Do M N.A revisit to cost aggregation in stereo matching:How far can we reduce its computational redundancy? //International Conference on Computer Vision.Barcelona:IEEE Computer Society,2011:1567-1574
[7]  Hirschmuller H,Scharstein D.Evaluation of stereo matching costs on Images with radiometric differences[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(9):1582-1599
[8]  Dongbo M,Jiangbo L,Do M N.A revisit to cost aggregation in stereo matching:How far can we reduce its computational redundancy? //International Conference on Computer Vision.Barcelona:IEEE Computer Society,2011:1567-1574
[9]  Hirschmuller H,Scharstein D.Evaluation of stereo matching costs on Images with radiometric differences[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(9):1582-1599
[10]  Yang Q.A non-local cost aggregation method for stereo matching //IEEE Conference on Computer Vision and Pattern Recognition.Providence,Rhode Island:IEEE Computer Society,2012:1402-1409
[11]  Yang Q.A non-local cost aggregation method for stereo matching //IEEE Conference on Computer Vision and Pattern Recognition.Providence,Rhode Island:IEEE Computer Society,2012:1402-1409
[12]  Perreault S,Hebert P.Median filtering in constant time[J].IEEE Transactions on Image Processing,2007,16(9):2389-2394
[13]  Perreault S,Hebert P.Median filtering in constant time[J].IEEE Transactions on Image Processing,2007,16(9):2389-2394
[14]  Scharstein D,Szeliski R.A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[J].International Journal of Computer Vision,2002,47(1):7-42
[15]  Tomasi C,Manduchi R.Bilateral filtering for gray and color images //International Conference on Computer Vision.Bombay,India:IEEE Computer Society,1998:839-846
[16]  Yoon K J,Kweon I S.Adaptive support-weight approach for correspondence search[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):650-656
[17]  Tomasi C,Manduchi R.Bilateral filtering for gray and color images //International Conference on Computer Vision.Bombay,India:IEEE Computer Society,1998:839-846
[18]  Hu Weidong,Zhang Kang,Sun Lifeng,et al.Virtual support window for adaptive-weight stereo matching //Visual Communications and Image Processing.Tainan:IEEE Circuits and Systems Society,2011:1-4
[19]  Kowalczuk J,Psota E T,Perez L C.Real-time stereo matching on CUDA using an iterative refinement method for adaptive support-weight correspondences[J].IEEE Transactions on Circuits and Systems for Video Technology,2013,23(1):94-104
[20]  Hu Weidong,Zhang Kang,Sun Lifeng,et al.Virtual support window for adaptive-weight stereo matching //Visual Communications and Image Processing.Tainan:IEEE Circuits and Systems Society,2011:1-4
[21]  Kowalczuk J,Psota E T,Perez L C.Real-time stereo matching on CUDA using an iterative refinement method for adaptive support-weight correspondences[J].IEEE Transactions on Circuits and Systems for Video Technology,2013,23(1):94-104
[22]  Klaus A,Sormann M,Karner K.Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure //International Conference on Pattern Recognition.Hong-kong:International Association for Pattern Recognition,2006:15-18
[23]  Scharstein D,Szeliski R.Middlebury stereo vision .(2012-04-05) .http://vision.middlebury.edu/stereo/

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