8 March 2018 Affine invariant feature extraction based on the shape of local support region
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 1060904 (2018) https://doi.org/10.1117/12.2282275
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
Feature extraction is an important step in image feature matching. And the repeatability of features is particularly crucial. The perspective deformation of images can decrease the repeatability of features. This paper introduces a feature extraction method which can improve the repeatability of features when notable perspective deformation exists. First, initial feature points are extracted by the classical Harris algorithm. Then a local support region is extracted for every initial feature point. Affine rectification parameters can be calculated based on the shape of the support region. Then the image patch around a feature point is resampled using these affine rectification parameters. The final feature points are extracted and described on the resampled image patches. The repeatability of the final features is much better than initial features thanks to the affine rectification. And the feature descriptors obtained on the resampled image patches are better to be used in image matching.
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Luping Lu, Yong Zhang, "Affine invariant feature extraction based on the shape of local support region", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060904 (8 March 2018); doi: 10.1117/12.2282275; https://doi.org/10.1117/12.2282275
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