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离散形状的特征区域匹配识别 被引量:3

LOCAL MATCH RECOGNITION OF DISCRETE SHAPE
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摘要 本文用形心到外轮廓的距离序列表示形状,从而将二维的形状分析问题转化成较简单的一维波形曲线的分析问题。对于这种波形曲线的识别,提出了一种特征区域的局部匹配识别算法。实验证明,本文方法具有较高的识别率,且该算法是平移、旋转和比例变化不变的。 Target shapes are represented by the sequence of distances between the centroid and contour in this paper. Therefore, the problem of two-dimensional target shape analysis is simplified as the one of one-dimensional waveform a (?)alysis. And an algorithm of local match recognition is proposed. Experiments show that the algorithm has high correct recognition ra(?)io, and is invariant to translation, rotation and scale.
出处 《自动化学报》 EI CSCD 北大核心 1993年第1期122-125,共4页 Acta Automatica Sinica
关键词 形状识别 匹配 离散形状 特征区域 Shape recognition match target.
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参考文献2

  • 1卢汉清,计算机杂志,1991年,19卷,116页
  • 2Hu M K,Proc IRE,1961年,49卷,14页

同被引文献8

  • 1文志强,蔡自兴.Mean Shift算法的收敛性分析[J].软件学报,2007,18(2):205-212. 被引量:47
  • 2Christof Ridder, Olaf Munkelt, and Harald Kirchner. " Adaptive Background Estimation and Foreground Detection using Kalman-Filtering" , In Int. Conf. on Recent Advances in Mechatronics, 1995, pp 193-199.
  • 3Cheng Y. Mean shift, mode seeking and clustering [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995,17(8): 790-799.
  • 4Fukunaga K, Hostetler L D.The estimation of the gradient of a density function, with applications in pattern recognition [J].IEEETrans Info Theory, 1975, IT-2 h 32-40.
  • 5Comaniciu D, Meer P.Mean Shift analysis and application[C]//Proceedings of the Seventh IEEE International Conference, ComputerVision, 1999, 2:1197- 1203.
  • 6Comaniciu D, Ramesh V, Meer P.Real-time tracking of non-rigidobjects using Mean Shift[C]//Computer Vision and Pattern Recognition (CVPR). 2000: 142-151.
  • 7Changjiang Yang, Ramani Duraiswami, Larry Davis. Efficient Mean-Shift Tracking via a New Similarity Measure [J]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.
  • 8Lei fei, Zhao Xiaoxia. Adaptive Background Estimation of Underwater Using Kalman-Filtering[C]. The 3rd International Congress on Image and Signal Processing. 16-18 Oct. 2010:64 - 67.

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