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A Fast Image Stitching Algorithm Based on Improved SURF

机译:基于改进SURF的快速图像拼接算法

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摘要

A fast image stitching algorithm based on improved speeded up robust feature (SURF) is proposed to overcome the real-time performance and robustness of the original SURF based stitching algorithms. The machine learning method is adopted to build a binary classifier, which identify the key feature points extracted by SURF and remove the non-key feature points. In addition, the RELIEF-F algorithm is used for dimension reduction and simplification of the improved SURF descriptor to achieve image registration. The threshold-based weighted fusion algorithm is used to achieve seamless image stitching. Finally, several experiments are conducted to verify the real-time performance and robustness of the improved algorithm.
机译:提出了一种基于改进的快速鲁棒特征(SURF)的快速图像拼接算法,以克服原始基于SURF的拼接算法的实时性和鲁棒性。采用机器学习方法构建二元分类器,识别由SURF提取的关键特征点,并去除非关键特征点。此外,RELIEF-F算法用于缩小尺寸并简化改进的SURF描述符以实现图像配准。基于阈值的加权融合算法用于实现无缝图像拼接。最后,进行了一些实验,以验证改进算法的实时性能和鲁棒性。

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