期刊文献+

医用光学中基于局部特征的高效稳健立体匹配算法 被引量:2

High Efficiency Feature-Based and Robust Local Stereo Matching Algorithm in Medical Optics
原文传递
导出
摘要 医用光学中为快速消除双目立体匹配歧义,提出一种基于局部特征、稳健高效的两步立体匹配方法。用局部二进制模板/对比度(LBP/C)纹理分析描述图像纹理并构造初始匹配成本;仅在垂直、水平方向内自适应地分配权值,消除匹配特征的相似歧义性,并行双通地聚合匹配成本;由赢者通吃法得到初始视差。为消除弱纹理、重复纹理和遮挡等引起的歧义,视差求精方案包括并行双通校准视差、基于映射检测遮挡、基于极线最小二乘拟合填充遮挡。实验表明,该算法计算效率高、结构简单,易于实现并消除匹配歧义,得到精度较高且分段平滑的视差。 A feature-based two-step local stereo matching with high efficiency is presented to eliminate the ambiguity of binocular stereo problem in medical optics. The local binary pattern/constrast (LBP/C) textures describing local features are firstly analyzed for both stereo pairs, and the initial matching costs is constructed from the features. Two-pass aggregation with adaptive support weight (ASW) for the costs is adopted only in the vertical and horizontal directions, which efficiently resolves ambiguity in feature matching. Initial disparity map is obtained via local Winner-Takes-All optimization from them. To solve the ambiguity from other aspects including textureless, repetitive patterns and occlusion etc., disparity refining procedure consists sequentially of three steps: two-pass ASW-based disparity calibration, warping-based occlusion detection and epipolar-line-based least square fitting to handle occlusion. The experimental results indicate that this approach with texture cues and low complexity has high efficiency and concise structure, being easily implemented and eliminting ambiguity, so that it can obtain comparably accurate and piecewise smooth dense disparity map effectively.
出处 《激光与光电子学进展》 CSCD 北大核心 2010年第5期100-108,共9页 Laser & Optoelectronics Progress
关键词 医用光学 机器视觉 双目立体匹配 局部二进制模板/对比度纹理 双通自适应支持权值 双通视差校准 最小二乘拟合 medical optics machine vision binocular stereo matching local binary pattern/constrast texture two-pass adaptive support weight two-pass disparity calibration least square fitting
  • 相关文献

参考文献17

  • 1Boguslaw Cyganek,J. Paul Siebert. An introduction to 3D computer vision techniques and algorithms[M]. West Sussex:John Wiley and Sons,Ltd. 2009. 366-397.
  • 2Daniel Scharstein,Richard Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[J]. International Journal of Computer Vision,2002,47(1/2/3):7-42.
  • 3Stan Birchfield,Carlo Tomasi. A pixel dissimilarity measure that is insensitive to image sampling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(4):401-406.
  • 4Timo Ojala,Matti Pietik?inen,Topi M?enp??. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
  • 5Kuk-Jin Yoon,In So Kweon. Adaptive support-weight approach for correspondence search[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):650-656.
  • 6Gu Zheng,Su Xianyu,Liu Yuankun et al.. Local stereo matching with adaptive support-weight,rank transform and disparity calibration[J]. Pattern Recognition Letters,2008,29(9):1230-1235.
  • 7顾征,苏显渝.三目自适应权值立体匹配和视差校准算法[J].光学学报,2008,28(4):734-738. 被引量:14
  • 8Federico Tombari, Stefano Mattoccia, Luigi Di Stefano. Segmentation-based adaptive support for accurate stereo correspondence [C]. IEEE Pacific-Rim Symposium on lmage and Video Technology, 2007. 427-438.
  • 9刘天亮,罗立民.一种基于分割的可变权值和视差估计的立体匹配算法[J].光学学报,2009,29(4):1002-1009. 被引量:11
  • 10Jong Dae Oh,Siwei Ma,C.-C. Jay Kuo. Stereo matching via disparity estimation and surface modeling [C]. IEEE International Conference on Computer Vision and Pattern Recognition,2007. 1696-1703.

二级参考文献31

共引文献23

同被引文献21

  • 1RICHARD S. Computer vision: Algorithms and applications [ M ].New York : Springer,2010:533 - 650.
  • 2DANIEL S,RICHARD S. A taxonomy and evaluation of dense two- frame stereo correspondence algorithms [ J ]. International Journal of Computer Vision,2002,47 ( 1/3 ) :7 - 42.
  • 3STAN B, CARLO T. A pixel dissimilarity measure that is insensitive to image sampling [ J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998,20 (4) :401 - 406.
  • 4LOWED G. Distinctive image features from scale-invariant key- points [ J ]. International Journal of Computer Vision, 2004,60 (2) : 91 -110.
  • 5BAY H, ESS A, TUYTELAARS T, et al. SURF: Speeded up robust features [ J ]. Computer Vision and Image Understanding, 2008, 110(3) :346 -359.
  • 6ENGIN T,VINCENT L,PASCAL F. DAISY:An efficient dense de- scriptor applied to wide-baseline stereo [ J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32 ( 5 ) : 815 - 830.
  • 7YOON K J, KWEON I S. Adaptive support-weight approach for cor- respondence search [ J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28 (4) :650 - 656.
  • 8GU Zheng, SU Xianyu, LIU Yuankun, et al. Local stereo matching with adaptive support-weight, rank transform and disparity calibra- tion [ J ]. Pattern Recognition Letters,2008,29 ( 9 ) : 1230 - 1235.
  • 9FEDERICO T, STEFANO M, LUIGI D S. Segmentation-based adap- tive support for accurate stereo correspondence[ C ]//IEEE Pacific- Rim Symposium on Image and Video Technology. 2007:427 -438.
  • 10TREVOR H, ROBERT T, JEROME F. The elements of statistical learning:Data mining, inference and prediction [ M ]. New York: Springer Series in Statistics,2009:191 -194.

引证文献2

二级引证文献18

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部