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一种鲁棒的图像局部特征区域的描述方法 被引量:18

A Robust Method for Local Image Feature Region Description
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摘要 提出了一种鲁棒的图像局部特征区域的描述方法,即IWCS-LTP(Improved weighted center symmetric local trinarypattern)描述子.该方法对图像局部特征区域采用类似SIFT描述子的分块处理,可以使描述子包含更多的结构信息;采用ICS-LTP算子进行编码,可以在不大量增加描述子维数和计算量的同时对图像的梯度方向信息进行更具体的描述;采用加权纹理谱直方图计算方法可以使描述子包含图像的梯度幅值信息.大量的实验结果验证了该描述子的有效性. In this paper,a robust method for local image feature region description,which is called IWCS-LTP(improved weighted center symmetric local trinary pattern)descriptor,is proposed.It uses a SIFT-like grid that makes the descriptor contain more structural information.By using the ICS-LTP operator,the descriptor can have more information of the image gradient direction without increasing the dimension of the descriptor and computing burden.This method uses the weighted texture spectrum histogram to construct the descriptor to contain the image gradient-magnitude information. The effectiveness of the designed descriptor has been validated by extensive experiments.
出处 《自动化学报》 EI CSCD 北大核心 2011年第6期658-664,共7页 Acta Automatica Sinica
基金 国家自然科学基金(61005009 60973064) 河北省自然科学基金(F2010000437) 北京市教委重点学科(XK100080537)资助~~
关键词 局部特征区域 LBP算子 LTP算子 CS-LBP描述子 Local feature region LBP operator LTP operator CS-LBP descriptor
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参考文献20

  • 1Li J, Allinson N M. A comprehensive review of current local features for computer vision. Neurocomputing, 2008, 71(10- 12): 1771-1787.
  • 2陈尔学,李增元,田昕,李世明.尺度不变特征变换法在SAR影像匹配中的应用[J].自动化学报,2008,34(8):861-868. 被引量:24
  • 3庄严,陈东,王伟,韩建达,王越超.移动机器人基于视觉室外自然场景理解的研究与进展[J].自动化学报,2010,36(1):1-11. 被引量:21
  • 4蔺海峰,马宇峰,宋涛.基于SIFT特征目标跟踪算法研究[J].自动化学报,2010,36(8):1204-1208. 被引量:71
  • 5Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110.
  • 6Yan K, Sukthankar R. PCA-SIFT: a more distinctive repre- sentation for local image descriptors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recog- nition. Washington D.C., USA: IEEE, 2004. 506-513.
  • 7Mikolajczyk K, Schmid C. A performance evaluation of lo- cal descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1615-1630.
  • 8Lazebnik S, Schmid C, Ponce J. A sparse texture representa- tion using local affine regions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1265-1278.
  • 9Bay H, Tuytelaars T, Gool L V. SURF: speeded up robust features. In: Proceedings of the 9th European Conference on Computer Vision. Graz, Austria: Springer, 2006. 404-417.
  • 10Ojala T, Pietikainen M, Mgenpaa T. Multiresolution gray- scale and rotation invariant texture classification with local binary patterns. IENF, Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.

二级参考文献119

  • 1安如,王慧麟,徐大新,冯学智,周绍光,何凯.基于影像尺度空间表达与鲁棒Hausdorff距离的快速角点特征匹配方法[J].测绘学报,2005,34(2):101-107. 被引量:3
  • 2胡斌,何克忠.计算机视觉在室外移动机器人中的应用[J].自动化学报,2006,32(5):774-784. 被引量:16
  • 3Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Fort Collins, USA: IEEE, 1999. 23-25.
  • 4Wren C R, Azarbayejani A, Darrell T, Pentland A P. Pfinder: real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 780--785.
  • 5Monnet A, Mittal A, Paragios N, Visvanathan R. Background modeling and subtraction of dynamic scenes. In: Proceedings of the 9th International Conference on Computer Vision. Washington D.C., USA: IEEE, 2003. 1305-1312.
  • 6Elgammal A, Duraiswami R, Harwood D, Davis L S. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of IEEE, 2002, 90(7): 1151-1163.
  • 7Tuzel O, Porikli F, Meer P. A Bayesian approach to background modeling. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE, 2005. 58-65.
  • 8Kim H, Sakamoto R, Kitahara I, Toriyama T, Kogure K. Background subtraction using generalised Gaussian family model. IEEE Electronics Letters, 2008, 44(3): 189-190.
  • 9Mason M, Duric Z. Using histograms to detect and track objects in color video. In: Proceedings of the 30th Applied Imagery Pattern Recognition Workshop. Washington D.C., USA: IEEE, 2001. 154-159.
  • 10Matsuyama T, Ohya T, Habe H. Background subtraction for non-stationary scenes. In: Proceedings of Asian Conference on Computer Vision. Taipei, China: IEEE, 2000. 622-667.

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