Prof. Qian Chen Profile

Prof. Qian Chen

at Nanjing Univ. of Science and Technology

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Publications (1)

Proceedings Article | 21 August 2020 Poster + Paper

Feature point matching of infrared and visible image

Wuxin Li, Qian Chen, Guohua Gu, Hongyang Bai, Xiubao Sui

Proceedings Volume 11510, 1151029 (2020) https://doi.org/10.1117/12.2565259

KEYWORDS: Infrared imaging, Image processing

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Feature point matching has been widely applied in image registration, image fusion, remote sensing and other fields. The relation between the pixels of infrared images and pixels of visible images is complex due to the images were taken by different sensors. Different sensors images also contain some common information which can been depended to achieve point matching. Scale Invariant Feature Transform (SIFT) algorithm is an effective and popular feature extraction algorithm. SIFT algorithm can be used in point matching, it can get feature descriptor vectors of the feature points which extracted from the images. But in some scenes, SIFT algorithm can’t achieve the accurate feature point matching. Different from SIFT algorithm, Edge-Oriented-Histogram (EOH) algorithm characters the orientation information of the edge and EOH feature descriptor can integrate the boundary information in different directions around the feature points, so that we can realize the describe of the edge direction and amplitude by EOH algorithm. To achieve the accurate feature point matching of infrared image and visible image, we propose a feature point matching method based on SIFT algorithm and EOH algorithm. Firstly, we use image enhancement to increase contrast of the image and then we use SIFT algorithm to extract feature points. Next, we utilize the EOH algorithm to get the 80 bins descriptors of the feature points which detected by SIFT algorithm. We calculate Euclidean distance to get the similarity of the descriptors to achieve point matching. In order to improve the matching accuracy, we adopt Random Sample Consensus (RANSAC) algorithm to eliminate the mismatching points. Nevertheless, RANSAC can’t get all correct points, we use another measure of Euclidean to select correct pairs, then we combine the two parts to match the feature points again. Experimental results demonstrate that our method can effectively improve the accuracy of matching and find more correct matching point pairs.

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