高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于信息论的KL-Reg点云配准算法

秦红星 徐雷

秦红星, 徐雷. 基于信息论的KL-Reg点云配准算法[J]. 电子与信息学报, 2015, 37(6): 1520-1524. doi: 10.11999/JEIT141248
引用本文: 秦红星, 徐雷. 基于信息论的KL-Reg点云配准算法[J]. 电子与信息学报, 2015, 37(6): 1520-1524. doi: 10.11999/JEIT141248
Qin Hong-xing, Xu Lei. Information Theory Based KL-Reg Point Cloud Registration[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1520-1524. doi: 10.11999/JEIT141248
Citation: Qin Hong-xing, Xu Lei. Information Theory Based KL-Reg Point Cloud Registration[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1520-1524. doi: 10.11999/JEIT141248

基于信息论的KL-Reg点云配准算法

doi: 10.11999/JEIT141248
基金项目: 

国家自然科学基金青年科学基金(61100113),国家教育部留学归国基金教外司留[2012]940号,重庆市首批青年骨干教师项目(渝教人(2011)31号),重庆市基础与前沿研究计划项目(cstc2013jcyjA 40062),重庆邮电大学学科引进人才基金(A2010-12)和重庆市研究生科研创新项目(CYS14142)资助课题

Information Theory Based KL-Reg Point Cloud Registration

  • 摘要: 针对含有高噪声、体外点及不完整点云数据的配准失效问题,该文提出以信息论为理论基础,相对熵度量点云相似度的KL-Reg算法。该算法不需要显式地建立对应关系,首先将点云数据建模为高斯混合模型,然后用相对熵度量高斯混合模型间的分布距离,最后通过最小化分布距离计算模型变换。实验结果表明所提的KL-Reg算法配准精度高、稳定性强。
  • Zitova B and Flusser J. Image registration methods: a survey[J]. Image and Vision Computing, 2003, 21(11): 977-1000.
    Lian Z, Godil A, Bustos B, et al.. A comparison of methods for non-rigid 3D shape retrieval[J]. Pattern Recognition, 2013, 46(1): 449-461.
    Liu M, Vemuri B C, Amari S I, et al.. Shape retrieval using hierarchical total bregman soft clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(12): 2407-2419.
    Besl P J and McKay N D. Method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 586-606.
    Tagliasacchi A, Bouaziz S, and Pauly M. Sparse iterative closest point[J]. Computer Graphics Forum, 2013, 32(5): 113-123.
    Tam G K L, Cheng Z Q, Lai Y K, et al.. Registration of 3D point clouds and meshes: a survey from rigid to nonrigid[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(7): 1199-1217.
    Jian B and Vemuri B C. Robust point set registration using gaussian mixture models[J]. IEEE Transactions on Pattern
    Analysis and Machine Intelligence, 2011, 33(8): 1633-1645.
    Myronenko A and Song X. Point set registration: coherent point drift[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(12): 2262-2275.
    Tsin Y and Kanade T. A correlation-based approach to robust point set registration[C]. Computer Vision-ECCV 2004. Springer Berlin Heidelberg, Prague, 2004: 558-569.
    Granger S and Pennec X. Multi-scale EM-ICP: a fast and robust approach for surface registration[C]. Computer VisionECCV 2002, Springer Berlin Heidelberg, Copenhagen, 2002: 418-432.
    Hermans J, Smeets D, Vandermeulen D, et al.. Robust point set registration using EM-ICP with information-theoretically optimal outlier handling[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado, USA, 2011: 2465-2472.
    Lipman Y, Yagev S, Poranne R, et al.. Feature matching with bounded distortion[J]. ACM Transactions on Graphics, 2014, 33(3), DOI: 10.1145/2602142.
    Paulus S, Dupuis J, Mahlein A K, et al.. Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping[J]. BMC Bioinformatics, 2013, 14(1), DOI: 10.1186/147-2105-14-238.
    Zeng Y, Wang C, Gu X, et al.. A generic deformation model for dense non-rigid surface registration: a higher-order MRF- based approach[C]. IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, 2013: 3360-3367.
    Hou T and Qin H. Robust dense registration of partial nonrigid shapes[J]. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(8): 1268-1280.
    Lombaert H, Grady L, Polimeni J R, et al.. FOCUSR: feature oriented correspondence using spectral regularization(a method for precise surface matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, Australia, 2013, 35(9): 2143-2160.
    Chang W and Zwicker M. Automatic registration for articulated shapes[J]. Computer Graphics Forum, 2008, 27(5): 1459-1468.
    Bronstein M M and Kokkinos I. Scale-invariant heat kernel signatures for non-rigid shape recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, 2010: 1704-1711.
    Sun J, Ovsjanikov M, and Guibas L. A concise and provably informative multi-scale signature based on heat diffusion[J]. Computer Graphics Forum, 2009, 28(5): 1383-1392.
    Hershey J R and Olsen P A. Approximating the Kullback Leibler divergence between Gaussian mixture models[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu, 2007(4): 317-320.
    Karaboga D, Gorkemli B, Ozturk C, et al.. A comprehensive survey: artificial bee colony (ABC) algorithm and applications[J]. Artificial Intelligence Review, 2014, 42(1): 21-57.
  • 加载中
计量
  • 文章访问数:  1719
  • HTML全文浏览量:  121
  • PDF下载量:  687
  • 被引次数: 0
出版历程
  • 收稿日期:  2014-09-25
  • 修回日期:  2015-02-27
  • 刊出日期:  2015-06-19

目录

    /

    返回文章
    返回