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Type: Journal article
Title: Accelerated guided sampling for multistructure model fitting
Author: Lai, T.
Wang, H.
Yan, Y.
Chin, T.J.
Zheng, J.
Li, B.
Citation: IEEE Transactions on Cybernetics, 2020; 50(10):4530-4543
Publisher: IEEE
Issue Date: 2020
ISSN: 2168-2267
Statement of
Taotao Lai, Hanzi Wang, Yan Yan, Tat-Jun Chin, Jin Zheng, Bo Li
Abstract: The performance of many robust model fitting techniques is largely dependent on the quality of the generated hypotheses. In this paper, we propose a novel guided sampling method, called accelerated guided sampling (AGS), to efficiently generate the accurate hypotheses for multistructure model fitting. Based on the observations that residual sorting can effectively reveal the data relationship (i.e., determine whether two data points belong to the same structure), and keypoint matching scores can be used to distinguish inliers from gross outliers, AGS effectively combines the benefits of residual sorting and keypoint matching scores to efficiently generate accurate hypotheses via information theoretic principles. Moreover, we reduce the computational cost of residual sorting in AGS by designing a new residual sorting strategy, which only sorts the top-ranked residuals of input data, rather than all input data. Experimental results demonstrate the effectiveness of the proposed method in computer vision tasks, such as homography matrix and fundamental matrix estimation.
Keywords: Hypothesis generation; keypoint matching scores; multiple structures; residual sorting; robust model fitting
Rights: c 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
DOI: 10.1109/TCYB.2018.2889908
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Computer Science publications

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