Localization of subastral point based on matching between salient regions

Haibo Li; Yunfeng Cao; Meng Ding; Likui Zhuang; Jiang Tao; Zhouyu Zhang; Peiyi Zhong

doi:10.1117/12.2286727

15 November 2017 Localization of subastral point based on matching between salient regions

Haibo Li, Yunfeng Cao, Meng Ding, Likui Zhuang, Jiang Tao, Zhouyu Zhang, Peiyi Zhong

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 1060512 (2017) https://doi.org/10.1117/12.2286727
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

Abstract

To determine the position of the subastral point in the base map and reduce the computational burden, an approach was developed based on scale invariant feature transform (SIFT) and match between salient regions. The salient regions of the base map can be determined by manual or algorithm in advance. And also, the salient regions of the descent image can be obtained by saliency computation. The extraction of SIFT feature was only performed on the salient regions of the base map and the descent image. These feature points were used to match between the two images. The method of maximum likelihood estimation sample consensus (MLESAC) was employed to eliminate the wrong matches. Then the correct matching points were used to determine the transform matrix between the base map and the descent image. The position of the probe can be predefined in the descent image. Through the transform matrix, the position of the subastral point can be determined in the base map. The experimental results demonstrate that the proposed approach can determine the position of the subastral point simply by matching in the salient regions rather than traversing the entire image to search for the matching points so as to reduce the cost of comparing all regions in two images.

Citation Download Citation

Haibo Li, Yunfeng Cao, Meng Ding, Likui Zhuang, Jiang Tao, Zhouyu Zhang, and Peiyi Zhong "Localization of subastral point based on matching between salient regions", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060512 (15 November 2017); https://doi.org/10.1117/12.2286727

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