Robust Bayesian cameras motion estimation using random sampling

TitleRobust Bayesian cameras motion estimation using random sampling
Publication TypeConference Papers
Year of Publication2004
AuthorsQian G, Chellappa R, Zheng Q
Conference NameImage Processing, 2004. ICIP '04. 2004 International Conference on
Date Published2004/10//
Keywords3D, baseline, Bayesian, CAMERAS, cameras;, coarse-to-fine, consensus, density, estimation;, feature, function;, hierarchy, image, images;, importance, matching;, MOTION, posterior, probability, probability;, processing;, random, RANSAC;, real, realistic, sample, sampling;, scheme;, sequences;, stereo, strategy;, synthetic, wide

In this paper, we propose an algorithm for robust 3D motion estimation of wide baseline cameras from noisy feature correspondences. The posterior probability density function of the camera motion parameters is represented by weighted samples. The algorithm employs a hierarchy coarse-to-fine strategy. First, a coarse prior distribution of camera motion parameters is estimated using the random sample consensus scheme (RANSAC). Based on this estimate, a refined posterior distribution of camera motion parameters can then be obtained through importance sampling. Experimental results using both synthetic and real image sequences indicate the efficacy of the proposed algorithm.