Bayesian perspective for the registration of multiple 3D views

• Published in: Computer vision and image understanding Vol. 118; pp. 84 - 96
• Main Authors: Mateo, X., Orriols, X., Binefa, X.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.01.2014; Elsevier
• Subjects: 3D registration; Algorithms; Applied sciences; Artificial intelligence; Bayesian analysis; Computer science; control theory; systems; Computer vision; Exact sciences and technology; Expectation Maximization algorithm; Formulations; Pairwise correspondences; Pattern recognition. Digital image processing. Computational geometry; Representations; Robustness; State of the art; Three dimensional; 3D registration; Expectation Maximization algorithm; Pairwise correspondences; Bayes estimation; Image registration; Computer vision; Image processing; Multiple view; Missing data; Experimental result; Concatenation; Model matching; Tridimensional image; Robustness; Reliability; Incomplete information; EM algorithm; Algorithm analysis; Posterior distribution
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1016/j.cviu.2013.09.003

•Use of a Bayesian framework allows tolerance to incorrect pairwise registrations.•Correspondence weights vary depending on their reliability, until a specific criteria is achieved.•Results show that an important improvement is achieved in objects with degraded correspondences.•Also in cases without degraded correspondences, the proposed method improves the accuracy in comparison to other methods. The registration of multiple 3D structures in order to obtain a full-side representation of a scene is a long-time studied subject. Even if the multiple pairwise registrations are almost correct, usually the concatenation of them along a cycle produces a non-satisfactory result at the end of the process due to the accumulation of the small errors. Obviously, the situation can still be worse if, in addition, we have incorrect pairwise correspondences between the views. In this paper, we embed the problem of global multiple views registration into a Bayesian framework, by means of an Expectation–Maximization (EM) algorithm, where pairwise correspondences are treated as missing data and, therefore, inferred through a maximum a posteriori (MAP) process. The presented formulation simultaneously considers uncertainty on pairwise correspondences and noise, allowing a final result which outperforms, in terms of accuracy and robustness, other state-of-the-art algorithms. Experimental results show a reliability analysis of the presented algorithm with respect to the percentage of a priori incorrect correspondences and their consequent effect on the global registration estimation. This analysis compares current state-of-the-art global registration methods with our formulation revealing that the introduction of a Bayesian formulation allows reaching configurations with a lower minimum of the global cost function.