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    题名: Robust trifocal tensor constraints for structure from motion estimation
    作者: Hor, Maw-Kae
    何瑁鎧
    Chan, Kai-Hsuan
    Tang, C.-Y.
    Wu, Y.-L.
    詹凱軒
    贡献者: 資科系
    关键词: Accurate estimation;Camera parameter;Estimation errors;Global optimization problems;Ground truth data;Multi-view stereo;Optimization solution;Orthogonal array;Reprojection error;Structure from motion;Trifocal tensor;Cameras;Errors;Estimation;Motion estimation;Particle swarm optimization (PSO);Pixels;Tensors;Parameter estimation
    日期: 2013
    上传时间: 2015-05-21 17:25:31 (UTC+8)
    摘要: It is important to estimate accurate camera parameters in multi-view stereo. In this paper, we use three-view relations, the trifocal tensor, to improve the Bundler, a popular structure from motion (SfM) system, for estimating accurate camera parameters. We propose a novel method: the Robust Orthogonal Particle Swarm Optimization (ROPSO) to estimate a robust and accurate trifocal tensor. In ROPSO, we formulate the trifocal tensor estimation as a global optimization problem and use the particle swarm optimization (PSO) for parameter searching. The orthogonal array is used to select the representative initial particles in PSO for more stable results. In the experiments, we use simulated and real ground truth data for statistical analysis. The experimental results show that the proposed ROPSO can achieve more accurate estimation of the trifocal tensor than the traditional methods and has higher probability to find the optimization solution than the traditional methods. Based on the trifocal tensor estimated by the proposed method, the SfM estimation errors can effectively be reduced. The average reprojection errors are reduced from 21.5 pixels to less than 1 pixel. © 2013 Elsevier B.V. All rights reserved.
    關聯: Pattern Recognition Letters, 34(6), 627-636
    数据类型: article
    DOI 連結: http://dx.doi.org/10.1016/j.patrec.2012.12.023
    DOI: 10.1016/j.patrec.2012.12.023
    显示于类别:[資訊科學系] 期刊論文

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