Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/146262
Title: Robust self-calibration from single image using RANSAC
Authors: Wu Q.
Shao T.-C.
Chen T. 
Issue Date: 2007
Citation: Wu Q., Shao T.-C., Chen T. (2007). Robust self-calibration from single image using RANSAC. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4841 LNCS (PART 1) : 230-237. ScholarBank@NUS Repository.
Abstract: In this paper, a novel approach for the self-calibration of single image is proposed. Unlike most existing methods, we can obtain the intrinsic and extrinsic parameters based on the information of restricted image points from single image. First, we show how the vanishing point, vanishing line and foot-to-head plane homology can be used to obtain the calibration parameters and then we show our approach how to efficiently adopt RANSAC to estimate them. In addition, noise reduction is proposed to handle the measurement uncertainties of input points. Results in synthetic and real scenes are presented to evaluate the performance of the proposed method.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/146262
ISBN: 9783540768579
ISSN: 03029743
Appears in Collections:Staff Publications

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