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Proceedings Paper

Turnover and shape filter based feature matching for image stitching
Author(s): Shuang Song; Xinguo He; Lin He
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Paper Abstract

This work intends to deal with the problem of misalignment in image stitching caused by small overlap area. To reduce mismatches between matched features pairs in two connected images, random sample consensus (RANSAC) [1] is usually adopted, which works under the assumption that the sampling of matched feature points with the largest number of inliers should be utilized to compute geometric matrix. However, this assumption does not hold in the case of small overlap area between the connected images, as compressing or turning over the image may result in better spatial consistency of matched feature points. Therefore, we propose a turnover and shape filter based feature matching method for image stitching. In the method, a turnover and shape filter is firstly used to filter out the samplings resulted from turnover and compression, which is then connected to RANSAC to yield final inliers. Experimental results from real-world datasets validate the effectiveness of our method.

Paper Details

Date Published: 14 February 2020
PDF: 6 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301E (14 February 2020); doi: 10.1117/12.2539406
Show Author Affiliations
Shuang Song, South China Univ. of Technology (China)
Xinguo He, South China Univ. of Technology (China)
Lin He, South China Univ. of Technology (China)


Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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