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Proceedings Paper

Sequence image matching using adaptive SIFT under complex environmental conditions
Author(s): Chunman Yan; Youfei Hao; Di Zhang; Jiahui Chen
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Paper Abstract

The scale invariant feature transform (SIFT) is one of effective methods for sequence image matching, but under complex environmental conditions such as illumination and blur, the matching rate is low, and the matching process becomes difficult. It is mainly because of the fixed threshold which results in the particular scenes are not considered. A new method with adaptive threshold is proposed for sequence image matching in this paper. Firstly, the statistical features of the sequence images are analyzed, then the comprehensive indicators of each statistical feature are calculated by the principal component analysis method, and finally, based on main influence factor and statistics features, the adaptive threshold prediction model is established. To test the efficiency of the proposed method, it is used for sequence image matching. The experimental results show that the adaptive threshold prediction model can be applied to many cases and improves the matching performance for sequence images under complex environmental conditions, especially for the sequence images under poor illumination.

Paper Details

Date Published: 14 August 2019
PDF: 7 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111792D (14 August 2019); doi: 10.1117/12.2539675
Show Author Affiliations
Chunman Yan, Northwest Normal Univ. (China)
Youfei Hao, Northwest Normal Univ. (China)
Di Zhang, Northwest Normal Univ. (China)
Jiahui Chen, Northwest Normal Univ. (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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