Acta Optica Sinica, Volume. 37, Issue 11, 1115003(2017)
Mixed Feature Extraction and Matching for Large Affine Scene
In order to improve accuracy of large-scale scene model in three-dimensional (3D) reconstruction, we extract two kinds of partial stable invariant features, under the premise of ensuring the efficiency of the algorithm, and use a multi-feature fusion method to match image features. Considering both problems of the joint modeling based on aerial and urban street images, we propose a matching method based on the two kinds of partial stable features. The method comprises the following steps. Firstly, we extract ASIFT (Affine Scale Invariant Feature Transform) feature points and MSER feature areas, and improve the MSER (Maximally Stable Extremal Regions) algorithm to get the two stable features described by SIFT (Scale Invariant Feature Transform) feature descriptor; secondly, we use the homography matrix to match features by the feature matching algorithm; finally, we parallelly optimize feature matching by using graphics processing unit(GPU). A large number of experiments and comparison results show that more than twice correct matching pairs can be obtained by the proposed algorithm than other two algorithms.
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Guofeng Tong, Yong Li, Nan Liu, Guangxu Ji. Mixed Feature Extraction and Matching for Large Affine Scene[J]. Acta Optica Sinica, 2017, 37(11): 1115003
Category: Machine Vision
Received: May. 16, 2017
Accepted: --
Published Online: Sep. 7, 2018
The Author Email: Liu Nan (1597196180@qq.com)