Share Email Print

Proceedings Paper

A RANSAC-ST method for image matching
Author(s): Fengman Jia; Zhizhong Kang
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Facing challenges of external environmental noise, it is necessary to find a robust, accurate and fast image-matching method. This paper proposed a method combining SIFT (Scale Invariant Feature Transform) algorithm and RANSACST (RANdom Sampling Consensus with Statistical Testing). RANSAC-ST algorithm is the improvement of RANSAC, which uses a strategy for best model determination in terms of the statistical characteristics of a deterministic mathematical model for hypothesis testing. It will generate a statistical histogram of all hypothesis fundamental matrices, and then the fundamental matrix whose convergence degree reaches the threshold is regarded as the best model. Experimental results show that with the proposed algorithm, the robustness and computation efficiency of correspondence matching can be effectively improved.

Paper Details

Date Published: 2 March 2016
PDF: 7 pages
Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 990115 (2 March 2016); doi: 10.1117/12.2234742
Show Author Affiliations
Fengman Jia, China Univ. of Geosciences (China)
Zhizhong Kang, China Univ. of Geosciences (China)

Published in SPIE Proceedings Vol. 9901:
2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015)
Cheng Wang; Rongrong Ji; Chenglu Wen, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?