结合分形特征及灰度相关的快速样本图像匹配算法

A new algorithm combined fractal signiture and gray  correlation for exemplar matching

  • 摘要: 样本图像的匹配广泛应用于图像的判读、情报的获取和目标识别等方面。基于图像区域和纹理特征的样本匹配算法由于不需要进行特征提取而成为研究的热点。但是这类方法计算复杂,实时性比较差。为此,本文提出了一种结合分形特征和灰度相关的由粗到精的样本图像匹配算法,并且提出了分形特征计算的快速计算方法。算法首先计算样本图像和原图像各像素点对应的分形特征值;然后利用分形特征进行样本图像的粗匹配,得到初步的配准结果;最后,利用灰度相关剔除粗匹配中错误的匹配区域,得到最后的匹配结果。大量实验表面,本文提出的算法是有效的。

     

    Abstract: The exemplar matching makes great sense in the analysis of image, acquisition of the information and target recognition etc.. For does not need to extract features firstly, algorithm based on region and texture becomes a hot issue. But the computational complexity of this kind algorithm is high. To solve the problem, a algorithm combined fractal signiture and gray correlation for exemplar registration is proposed in this paper. It is divided into three steps: Firstly, compute the fractal signatures of the pixels of the image, and gain the fractal signature of the exemplar according to its position in the image. Then, find the regions that maybe the same or similary to the exemplar image using the fractal signature. Finally, eliminate the false matching regions by use of gray correlation, and gain the final matching result. In addition, a fast method for computing fractal signature is also propose in the paper to improve the efficiency. Experiments show that the propose algorithm is effective and potent

     

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