Gaussian field consensus: A robust nonparametric matching method for outlier rejection
By a News Reporter-Staff News Editor at Journal of Robotics & Machine Learning -- Current study results on Pattern Analysis have been published. According to news reporting from Shanghai, People’s Republic of China, by VerticalNews journalists, research stated, “In this paper, we propose a robust method, called Gaussian Field Consensus (GFC), for outlier rejection from given putative point set matching correspondences. Finding correct correspondences (inliers) is a key component in many computer vision and pattern recognition tasks, and the goal of outlier (mismatch) rejection is to fit the transformation function that maps one feature point set to another.”
Financial support for this research came from National Natural Science Foundation of China.
The news correspondents obtained a quote from the research from Shanghai University, “Our GFC starts by inputting a putative correspondence set which is contaminated by many outliers, and the main task of our GFC is to identify the underlying true correspondences from outliers. Then we formulate this challenging problem by Gaussian Field nonparametric matching model which bases on the exponential distance loss and kernel method in a reproducing kernel Hilbert space. Next, We introduce a local linear constraint based on the regularization theory to preserve the topological structure of the feature points. Moreover, the sparse approximation is used to reduce the search space, in this way, we can handle a large number of points easily.”
According to the news reporters, the research concluded: “Finally, we test the GFC method on several real image datasets in the presence of outliers, where the experimental results show that our proposed method outperforms current state-of-the-art methods in most test scenarios.”
For more information on this research see: Gaussian field consensus: A robust nonparametric matching method for outlier rejection. Pattern Recognition , 2018;74():305-316. Pattern Recognition can be contacted at: Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, Oxon, England. (Elsevier - www.elsevier.com; Pattern Recognition - http://www.journals.elsevier.com/pattern-recognition/)
Our news journalists report that additional information may be obtained by contacting G. Wang, Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, People’s Republic of China. Additional authors for this research include Y.F. Chen and X.W. Zheng.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.patcog.2017.09.029. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-01-08), Investigators at Shanghai University Report Findings in Pattern Analysis (Gaussian field consensus: A robust nonparametric matching method for outlier rejection), Journal of Robotics & Machine Learning, 215, ISSN: 1944-186X, BUTTER® ID: 014943620
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