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A new algorithm of fast-generated panoramic images

机译:一种新的快速全景图像算法

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摘要

The new algorithm of fast-generated panoramic images this paper puts forward is to extract the feature points of images by the improved SIFT algorithm, and use Euclidean distance combining the K-D tree structure to realize the rapid initial feature matching. Then, based on these initial matching points and the theory of random sampling consistent algorithm, the purification of feature points is realized. At last, the introduction of correction coefficient makes it possible to eliminate fusion ghosts, and HIS space image fusion is applied in order to eliminate the brightness differences. It is verified by the experiments that on the premise of generation of quality guarantee, the new algorithm greatly improves the generation efficiency of panorama images.
机译:本文的快速生成全景图像的新算法向前提出了通过改进的SIFT算法提取图像的特征点,并使用组合K-D树结构的欧几里德距离来实现快速初始特征匹配。然后,基于这些初始匹配点和随机采样的理论一致算法,实现了特征点的净化。最后,校正系数的引入使得可以消除融合鬼,并且施加他的空间图像融合以消除亮度差异。通过实验验证,在生产质量保证的前提下,新算法大大提高了全景图像的发电效率。

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