Abstract
Despite the good results obtained in various applications, the performance of SIFT declined under conditions of insufficient illumination and blur imaging. The main reason is that SIFT key points are discarded if the DoG response is less than a fixed preset threshold. We proposed a dynamic threshold assign method based on both global and local information. Firstly Initial threshold is obtained according to contrast of GLCM, making it adaptive to insufficient illumination and blur imaging. Secondly, in order to control the number of feature points, the threshold is adjusted once again according to feature points’ distribution. So every feature point is assigned a different threshold adapting to surrounding situation. At last, the mismatch removing algorithm is also been improved by incorporating the global distribution context. Experiment results show that the improved SIFT algorithm is not only well adapted to low light and image blur, but also can adjust the number of feature points and reduce clustering effects.
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