Parallel voting RANSAC and its implementation on FPGA

被引：0

作者：

Jiang, Jie ^{[1
]}

Ling, Si-Rui ^{[1
]}

机构：

[1] Key Laboratory of Precision Optomechatronics Technology, Ministry of Education, Beihang University

来源：

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2014年 / 36卷 / 05期

关键词：

FPGA; Parallel computing; Random Sample Consensus (RANSAC);

D O I：

10.3724/SP.J.1146.2013.00962

中图分类号：

学科分类号：

摘要：

Random Sample Consensus (RANSAC) performs poor with the mass of data, high outliers ratio and complicated models. In this paper, a highly parallel voting version of RANSAC is presented. On the basis of parallelizing the hypothetical stage and generating multiple models simultaneously, a novel strategy of voting to determine whether a point belongs to inliers is proposed. Conventional search for the inliers relative to the best model is saved. On parallel platforms represented by FPGA, this algorithm can take advantage of the parallel architecture and characteristics to achieve deep-pipelined parallel computing. Experiments demonstrate the good robustness of the proposed algorithm and its considerable improvement of both speed and throughput.

引用

页码：1145 / 1150

页数：5

共 17 条

[1]

Fischler M.A., Bolles R.C., Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, 24, 6, pp. 381-395, (1981)

[2]

Chen F.-X., Wang R.-S., A new vanishing point detecting algorithm, Journal of Electronics & Information Technology, 28, 8, pp. 1458-1462, (2008)

[3]

Bhattacharya P., Gavrilova M., Improving RANSAC feature matching with local topological information, 2012 Ninth International Symposium on Voronoi Diagrams in Science and Engineering, pp. 17-23, (2012)

[4]

Baker C.L., Hoff W., DIRSAC: a directed sampling and consensus approach to quasi-degenerate data fitting, 2013 IEEE Workshop on Applications of Computer Vision, pp. 154-159, (2013)

[5]

El-Melegy M.T., RANSAC algorithm with sequential probability ratio test for robust training of feed-forward neural networks, Proceedings of International Joint Conference on Neural Networks, pp. 3256-3263, (2011)

[6]

Zhou J., Chen L.-T., Liu Q.-H., Et al., Fast and accurate RANSAC based on optimal sequential probability test and local optimization, Chinese Journal of Scientific Instrument, 33, 9, pp. 2037-2044, (2012)

[7]

Golban C., Szakats I., Nedevschi S., Stereo based visual odometry in difficult traffic scenes, 2012 Intelligent Vehicles Symposium, pp. 736-741, (2012)

[8]

Zhang W.-C., Li B., Deng H.-P., Et al., Distorted pupil localization in eye tracking, Journal of Electronics & Information Technology, 32, 2, pp. 416-421, (2010)

[9]

Xu M., Lu J., Distributed RANSAC for the robust estimation of three-dimensional reconstruction, IET Computer Vision, 6, 4, pp. 324-333, (2012)

[10]

Martelli S., Marzotto R., Colombari A., Et al., FPGA-based robust ellipse estimation for circular road sign detection, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 53-60, (2010)

← 1 2 →