Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99019
Title: An affine invariant feature detection method based on SIFT and MSER
Authors: Wang, Zhuping
Mo, Huiyu
Wang, Han
Wang, Danwei
Issue Date: 2012
Source: Wang, Z., Mo, H., Wang, H.,& Wang, D. (2012). An affine invariant feature detection method based on SIFT and MSER. 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA), 69 - 72.
Abstract: In this paper, an affine invariance feature detection method based on Scale Invariant Feature Transform (SIFT) and Maximally Stable Extremal Regions (MSER) is proposed. Classical SIFT algorithm is not robust to affine deformations, because it is based on DOG detector which extracts circle regions for keypoint location. In order to overcome this disadvantage, DOG detector in conventional SIFT algorithm is replaced by MSER detector which is robust to affine deformation. Then these regions are normalized and extracted using SIFT. Simulation studies are carried out to show the effectiveness of the proposed method to affine transform in comparison to traditional SIFT algorithm.
URI: https://hdl.handle.net/10356/99019
http://hdl.handle.net/10220/12871
DOI: http://dx.doi.org/10.1109/ICIEA.2012.6360699
metadata.item.grantfulltext: none
metadata.item.fulltext: No Fulltext
Appears in Collections:EEE Conference Papers

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