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The Research of SURF Image Matching Method Based on Region and Feature Information

机译:基于区域和特征信息的冲浪图像匹配方法研究

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According to the SURF feature matching dense matching interpolation algorithm to realize the problems in the complex dynamic background image. The dynamic camera captured the same environment as the object. SURF image is proposed based on the combination of region and the feature information matching method. Firstly, image gray-scale normalization. There may be a reduction the brightness of light due to the translational response point of view. And then add several constraints in the process of extracting SURF feature points. Not only can speed up the matching process, but also can reduce the false match rate. Finally, in order to detect the feature points as the seed point for regional growth, the seed region division of the region by using graph matching. The test show that the algorithm can obtain dense disparity images of good. After the camera shooting of different sizes of image feature matching. Experiments show that, Compared with the SIFT algorithm and the SURF algorithm, the algorithm reduces the number of feature points and matching points, and improves the matching accuracy and the matching accuracy.
机译:根据冲浪功能匹配密集匹配插值算法,实现复杂动态背景图像中的问题。动态相机捕获与对象相同的环境。基于区域和特征信息匹配方法的组合提出了冲浪图像。首先,图像灰度标准化。由于翻译响应的观点,可能会降低光的亮度。然后在提取冲浪特征点的过程中添加多个约束。不仅可以加快匹配过程,还可以减少假匹配率。最后,为了检测特征点作为区域生长的种子点,通过使用曲线匹配来检测区域生长的种子分割区域。该测试表明该算法可以获得良好的密集视差图像。相机拍摄不同大小的图像特征匹配。实验表明,与SIFT算法和冲浪算法相比,该算法减少了特征点和匹配点的数量,提高了匹配的精度和匹配精度。

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