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Image registration based on criteria of feature point pair mutual information

机译:基于特征点对互信息准则的图像配准

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Similarity measurement based on mutual information maximisation has been successful applied in image registration. However, it costs a lot of computation time and the interference of local maxima in the search process always makes the registration search into local maxima that may cause misregistration. In order to eliminate these shortcomings, a novel image registration method is presented in this study. In the method wavelet multi-scale product is calculated to extract the feature points and angle information of the two input images, then a new criterion of registration criterion of feature point pair mutual information is defined to acquire corresponding matching points. In the experiments, our method and image registration methods based on correlation criteria and alignment metric criteria are tested for comparison. Experimental results show that the registration of our method outperforms that of the other two methods and the registration errors are obviously reduced. The errors of coordinates are lower compared to the errors of 78% of the other two methods and the errors of rotation angle are lower compared to the errors of 6% of the other two methods. The seams of the registration image results are very smooth and the transition zones are very uniform.
机译:基于互信息最大化的相似度测量已成功应用于图像配准中。然而,这花费大量的计算时间,并且局部最大值在搜索过程中的干扰总是使配准搜索成为局部最大值,这可能导致配准失准。为了消除这些缺点,本研究提出了一种新颖的图像配准方法。该方法通过计算小波多尺度乘积来提取两个输入图像的特征点和角度信息,然后定义特征点对互信息的配准准则,以获取对应的匹配点。在实验中,我们测试了基于相关标准和对齐度量标准的方法和图像配准方法进行比较。实验结果表明,该方法的配准效果优于其他两种方法,配准误差明显降低。与其他两种方法的坐标误差相比,坐标误差要低78%,而与其他两种方法的坐标误差误差相比,旋转角误差要低6%。配准图像结果的接缝非常平滑,过渡区域非常均匀。

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