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首页> 外文会议>International Conference on Machine Learning and Cybernetics >SOLAR IMAGE MATCHING BASED ON IMPROVED FREAK ALGORITHM
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SOLAR IMAGE MATCHING BASED ON IMPROVED FREAK ALGORITHM

机译：基于改进的Freak算法的太阳能图像匹配

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FREAK algorithm has the defects of not having scale invariance and single feature point matching strategy, being prone to unsatisfactory results. Based on AKAZE and RANSAC algorithm, an improved FREAK algorithm is proposed, named AKAZE-FREAK. The image pyramid was constructed by using the nonlinear diffusion filter and the numerical solution was obtained by fast display diffusion (FED) to obtain the image point coordinates with sub-pixel precision. Then the feature points were described by the FREAK descriptors to distribute directions for the feature points. Finally, based on the initial matching of the hamming distance to the eigen vector, RANSAC is used to eliminate the error matching point. In this paper, this algorithm is applied to the recognition of repeated shooting solar images. After the improved algorithm is compared with the SIFT-FREAK, SURF-FREAK and FREAK algorithms, it shows an improvement in the image feature point matching accuracy with good robustness on the scale difference, illumination difference and rotation difference of the image. It also improves the accuracy of the judgment of the late repeat.
机译：Freak算法具有不具有规模不变性和单个特征点匹配策略的缺陷，易于不令人满意的结果。基于Akaze和Ransac算法，提出了一种改进的怪胎算法，名为Akaze-Freak。通过使用非线性扩散滤波器构建图像金字塔，并且通过快速显示扩散（FED）获得数值溶液，以获得具有子像素精度的图像点坐标。然后，频段描述符描述了特征点以分配特征点的方向。最后，基于汉明距离的初始匹配到特征向量，Ransac用于消除误差匹配点。本文应用了该算法应用于重复拍摄太阳能图像的识别。在将改进的算法与Sift-Freak，冲浪 - 怪胎和怪胎算法进行比较之后，它显示了图像特征点匹配精度的改进，具有良好的鲁棒性，在尺度差，照明差和图像的旋转差上。它还提高了判断重复的判断的准确性。

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