Fitting multiple projective models using clustering-based Markov chain Monte Carlo inference
作者：
Highlights：
• Assignment of projective models becomes a problem of probabilistic inference through clustering.
• A Markov network formulation that models data points in terms of projective relationships in two views is introduced.
• An algorithm that fits multiple varieties to data points is specified using MCMC based inference.
• Use of a global energy measure to capture the quality of convergence.
• Comparative results indicate less susceptibility to parameter tuning and increased accuracy of convergence.
摘要
•Assignment of projective models becomes a problem of probabilistic inference through clustering.•A Markov network formulation that models data points in terms of projective relationships in two views is introduced.•An algorithm that fits multiple varieties to data points is specified using MCMC based inference.•Use of a global energy measure to capture the quality of convergence.•Comparative results indicate less susceptibility to parameter tuning and increased accuracy of convergence.
论文关键词：Multiple model fitting,Clustering,Markov chain Monte Carlo,Two-view geometry,Markov random field
论文评审过程：Received 17 December 2013, Revised 19 October 2014, Accepted 20 October 2014, Available online 6 November 2014.
论文官网地址：https://doi.org/10.1016/j.imavis.2014.10.009
