In this paper, a novel image matching method is proposed in order to improve the performance of image registration, especially for blur images. Firstly, A set of Scale Invariant Feature Transform (SIFT) points are extracted. Secondly, in order to further improve the distinctiveness of the SIFT descriptors, three scale invariant concentric circular regions are applied to produce descriptors. Thirdly, for the purpose of decreasing the high dimensional and complexity of SIFT descriptors, The Local Preserving Projection (LPP) technic is applied to reduce the dimensions of the descriptors. Lastly, the Euclidean distance similarity measurements are used to obtain the results of matching feature points. The experimental results show that the novel image matching method can not only reduce the data amounts, but also improve the matching speed and the matching precision.

Zhi Huang, Yaran Yang. A novel and fast blurred image matching method. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 1: 20-26. https://doi.org/10.25236/AJCIS.2021.040104.

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