Abstract
A new local feature extraction method (BSPL) is proposed and applied to heterogeneous image matching to solve the problem that the traditional SIFT features have poor matching performance in heterogeneous image matching. A number of improvements have been made to ensure that common features of heterogeneous images can be extracted efficiently. The gradient histogram-equalized image is used as the input matching image; The bilateral filtering is used to construct the scale space pyramid to replace the Gaussian filtering of the traditional SIFT, which can make the details such as the edges of the image better preserved; PCA-based LDB descriptor is used as feature expression to improve the robustness of feature expression. Experimental results show that the proposed local feature descriptor has rotation and scale invariance, and effectively improves the number of matching points, matching accuracy, matching precision and matching adaptability, which is an effective infrared and visible image matching method.
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Zhang, L., Dai, M., Tian, J. (2019). Infrared and Visible Image Matching Algorithm Based on SIFT and LDB. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_11
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DOI: https://doi.org/10.1007/978-981-13-9917-6_11
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