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核密度估计在立体匹配中的应用 被引量：3

Application of Kernel Density Estimation in Stereo Matching
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摘要 提出了一种基于核密度估计相似性测度的立体匹配方法。将匹配基元中对应像素点的颜色差值在差值特征空间中进行核密度估计,并以此作为相似性测度。在此基础上建立全局能量函数,并采用改进的置信度传播算法进行视差图的求解。实验结果证明该相似性测度较传统方法有明显的改进。 A new stereo matcbing metbod based on Kernel Density Estimation（KDE） similarity is proposed. KDE of the color difference value between the corresponding matching elements is calculated in difference feature space and selected as the similarity measure. Based on this similarity measure, a global energy function is constructed and the final disparity map is obtained by the improved belief prupagation algorithm. Experimental results demonstrate the improvement of the measure compared with the traditional methods.
作者 牛君 李贻斌 宋锐
机构地区 山东大学控制科学与工程学院
出处 《计算机工程》 CAS CSCD 北大核心 2008年第2期40-42,共3页 Computer Engineering
基金 国家自然科学基金资助项目(60675044)
关键词 立体匹配 核密度估计 差值样本空间 置信度传播 stereo matching Kernel Density Estimation（KDE） difference sample space belief propagation
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献6

  • 1Hirschmuller H. Accurate and Efficient Stereo Processing by Semiglobal Matching and Mutual Information[C]//Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA: [s. n.], 2005.
  • 2Olson C F. Maximum-likelihood Image Matching[J]. IEEE Transao ctions on Pattern Analysis and Machine Intelligence, 2002, 24(6): 853-857.
  • 3Boykov Y, Veksler O, Zabih R, Fast Approximate Energy Minimization via Graph Cuts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001,23(11): 1222-1239.
  • 4Carlos L, Ben A, Sun Changming. Fast Stereo Matching by Iterated Dynamic Programming and Quadtree Subregioning[C]//Proc. of British Machine Vision Conference. Norwich, UK: [s. n.], 2004.
  • 5Sun Jian, Zheng Nanning, Shum H Y. Stereo Matching Using Belief Propagation[J1. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(7): 787-800.
  • 6Felzenszwalb P F, Huttcnlocher D R. Efficient Belief Propagation for Early Vision[C]//Proc. of the 2004 IEEE Computer Society Confcrence on Computer Vision and Pattern Recognition. New York USA: [s. n.], 2004.

同被引文献17

  • 1周秀芝,文贡坚,王润生.自适应窗口快速立体匹配[J].计算机学报,2006,29(3):473-479. 被引量：32
  • 2Boykov Y, Veksler O, Zabih R. Fast Approximate Energy Minimization via Graph Cuts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(11): 1222-1239.
  • 3Quan Long, Tan Ping, Zeng Gang, et al. Image-based Plant Modeling[J]. ACM Transactions on Graphics, 2006, 25(3): 599-604.
  • 4Haralick R M, Shanmugam K, Dinstein I H. Textural Features for Image Classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, 3(6): 610-621.
  • 5Scharstein 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.
  • 6Birchfield S, Tomasi C. A Pixel Dissimilarity Measure That ls Insensitive to Image Sampling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(4): 401-406.
  • 7Bobick A F, Intille S S. Large Occlusion Stereo[J]. International Journal of Computer Vision, 1999, 33(3): 181-200.
  • 8Scharstein D, Szelisti R. A Taxonomy and Evaluation of Dense Two-frame Stereo Correspondence Algorithms[J]. International Journal of Computer Vision, 2002, 47(1-3): 7-42.
  • 9Birchfield S, Tomasi C. A Pixel Dissimilarity Measure That is Insensitive to Image Sampling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(4): 401-406.
  • 10Sun Jian, Zheng Nanning. Stereo Matching Using Belief Propagation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(7): 787-800.

引证文献3

计算机工程
2008年 第2期

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