计算机应用 ›› 2014, Vol. 34 ›› Issue (3): 841-845.DOI: 10.11772/j.issn.1001-9081.2014.03.0841

• 虚拟现实与数字媒体 • 上一篇    下一篇

基于改进多尺度乘积LoG算子的仿射不变形状匹配算法

杜海静1,2,肖阳辉1,2,3,朱丹1,2,3,佟新鑫1,2,3   

  1. 1. 中国科学院 沈阳自动化研究所,沈阳110016;
    2. 中国科学院大学 计算机与控制学院,北京100049;
    3. 中国科学院 光电信息处理重点实验室,沈阳110016
  • 收稿日期:2013-08-23 修回日期:2013-10-22 出版日期:2014-03-01 发布日期:2014-04-01
  • 通讯作者: 杜海静
  • 作者简介:杜海静(1988-),女,河北唐山人,硕士研究生,主要研究方向:自动目标识别与跟踪;肖阳辉(1968-),男,河南信阳人,研究员,主要研究方向:光电成像、目标跟踪、目标识别;朱丹(1962-),男,辽宁沈阳人,研究员,主要研究方向:成像跟踪、实时图像处理、图像识别。

Affine-invariant shape matching algorithm based on modified multi-scale product Laplacian of Gaussian operator

DU Haijing1,2,XIAO Yanghui1,2,3,ZHU Dan1,2,3,TONG Xinxin1,2,3   

  1. 1. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;
    2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang Liaoning 110016, China;
    3. Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang Liaoning 110016, China
  • Received:2013-08-23 Revised:2013-10-22 Online:2014-03-01 Published:2014-04-01
  • Contact: DU Haijing

摘要:

目标在成像过程中发生的几何变形多数情况下可用仿射变换来描述。据此,提出一种利用角点进行仿射不变形状匹配的算法。首先引入多尺度乘积LoG(MPLoG)算子检测轮廓角点,并根据角点间距自适应地提取轮廓特征点,从而获取形状关键特征;为解决目标的仿射变形问题,采用Grassmann流形Gr(2,n)来表征和度量两形状之间的相似度;最后通过迭代式序列移位匹配算法来克服Grassmann流形对起始点的依赖并完成形状的匹配。对形状数据进行仿真实验的结果表明,所提算法能够有效地实现形状检索和识别,并对噪声有较强的鲁棒性。

关键词: 多尺度乘积LoG算子, 角点检测, Grassmann流形, 迭代式序列移位, 形状匹配

Abstract:

Geometric transforms of the object in the imaging process can be represented by affine transform in most situations. Therefore, a method for shape matching using corners was proposed. Firstly, the corner of contour using Multi-scale Product Laplacian of Gaussian (MPLoG) operator was detected, and the feature points based on corner interval were adaptively extracted to obtain the key feature of shape. In order to cope with affine transform, the similarity of two shapes on Grassmann manifold Gr(2,n) were represented and measured. Finally, the iterative sequence shift matching was adopted for overcoming the dependency of Grassmann manifold on the starting point, and achieving shape matching. The proposed algorithm was tested on the database of shapes. The simulation results show that the proposed method can achieve shape recognition and retrieval effectively, and it has strong robustness against noise.

Key words: Multi-scale Product Laplacian of Gaussian (MPLoG) operator, corner detection, Grassmann manifold, iterative sequence shift, shape matching

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