Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Accelerated guided sampling for multistructure model fitting|
|Citation:||IEEE Transactions on Cybernetics, 2020; 50(10):4530-4543|
|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.|
|Appears in Collections:||Aurora harvest 8|
Computer Science publications
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.