针对当前图像匹配方法的鲁棒性差、误配率较高及效率较低等不足,提出了基于三角网下的仿射不变几何约束的图像匹配算法。在尺度空间上通过Hessian矩阵对特征点进行检测,利用子块的三角特征与对角特征SURF(speeded up robust features)...针对当前图像匹配方法的鲁棒性差、误配率较高及效率较低等不足,提出了基于三角网下的仿射不变几何约束的图像匹配算法。在尺度空间上通过Hessian矩阵对特征点进行检测,利用子块的三角特征与对角特征SURF(speeded up robust features)机制进行改进,用于生成新的特征描述子,并通过定义阈值评估策略对图像特征点进行匹配,从而生成了初始匹配点;然后,引入Delaunay三角网,对初始匹配点进行聚类,以获取匹配三角形,将三角形以外的无效特征点剔除;最后,引入仿射不变几何约束,对匹配三角形进行细化,通过细化的匹配三角形获取最终的匹配特征点,有效剔除误配点,进一步提高配准精度。仿真结果表明,与当前图像匹配算法相比,所提算法具有更好的鲁棒性,且其具有更佳的匹配精度与效率,有效剔除了误配点。展开更多
Feature-based image matching algorithms play an indispensable role in automatic target recognition （ATR）. In this work, a fast image matching algorithm （FIMA） is proposed which utilizes the geometry feature of ext...Feature-based image matching algorithms play an indispensable role in automatic target recognition （ATR）. In this work, a fast image matching algorithm （FIMA） is proposed which utilizes the geometry feature of extended centroid （EC） to build affine invariants. Based on at-fine invariants of the length ratio of two parallel line segments, FIMA overcomes the invalidation problem of the state-of-the-art algorithms based on affine geometry features, and increases the feature diversity of different targets, thus reducing misjudgment rate during recognizing targets. However, it is found that FIMA suffers from the parallelogram contour problem and the coincidence invalidation. An advanced FIMA is designed to cope with these problems. Experiments prove that the proposed algorithms have better robustness for Gaussian noise, gray-scale change, contrast change, illumination and small three-dimensional rotation. Compared with the latest fast image matching algorithms based on geometry features, FIMA reaches the speedup of approximate 1.75 times. Thus, FIMA would be more suitable for actual ATR applications.展开更多
文摘针对当前图像匹配方法的鲁棒性差、误配率较高及效率较低等不足,提出了基于三角网下的仿射不变几何约束的图像匹配算法。在尺度空间上通过Hessian矩阵对特征点进行检测,利用子块的三角特征与对角特征SURF(speeded up robust features)机制进行改进,用于生成新的特征描述子,并通过定义阈值评估策略对图像特征点进行匹配,从而生成了初始匹配点;然后,引入Delaunay三角网,对初始匹配点进行聚类,以获取匹配三角形,将三角形以外的无效特征点剔除;最后,引入仿射不变几何约束,对匹配三角形进行细化,通过细化的匹配三角形获取最终的匹配特征点,有效剔除误配点,进一步提高配准精度。仿真结果表明,与当前图像匹配算法相比,所提算法具有更好的鲁棒性,且其具有更佳的匹配精度与效率,有效剔除了误配点。
基金Projects(2012AA010901,2012AA01A301)supported by National High Technology Research and Development Program of ChinaProjects(61272142,61103082,61003075,61170261,61103193)supported by the National Natural Science Foundation of ChinaProjects(B120601,CX2012A002)supported by Fund Sponsor Project of Excellent Postgraduate Student of NUDT,China
文摘Feature-based image matching algorithms play an indispensable role in automatic target recognition （ATR）. In this work, a fast image matching algorithm （FIMA） is proposed which utilizes the geometry feature of extended centroid （EC） to build affine invariants. Based on at-fine invariants of the length ratio of two parallel line segments, FIMA overcomes the invalidation problem of the state-of-the-art algorithms based on affine geometry features, and increases the feature diversity of different targets, thus reducing misjudgment rate during recognizing targets. However, it is found that FIMA suffers from the parallelogram contour problem and the coincidence invalidation. An advanced FIMA is designed to cope with these problems. Experiments prove that the proposed algorithms have better robustness for Gaussian noise, gray-scale change, contrast change, illumination and small three-dimensional rotation. Compared with the latest fast image matching algorithms based on geometry features, FIMA reaches the speedup of approximate 1.75 times. Thus, FIMA would be more suitable for actual ATR applications.
