Graduation Year

2009

Document Type

Thesis

Degree

M.S.C.S.

Degree Granting Department

Computer Science

Major Professor

Sudeep Sarkar, Ph.D.

Committee Member

Rangachar Kasturi, Ph.D.

Committee Member

Dmitry Goldgof, Ph.D.

Keywords

Similarity, Joint Feature Distributions, Jump Diffusion, Degeneracy, Epipolar Geometry

Abstract

The problem of epipolar geometry estimation together with correspondence establishment in case of wide baseline and large scale changes and rotation has been addressed in this work. This work deals with cases that are heavily noised by outliers. The jump diffusion MCMC method has been employed to search for the non-degenerate epipolar geometry with the highest probabilistic support of putative correspondences. At the same time, inliers in the putative set are also identified. The jump steps involve large movements guided by a distribution of similarity based priors while diffusion steps are small movements guided by a distribution of likelihoods given by the Joint Feature Distribution (JFD). The 'best so far' samples are accepted in accordance to Metropolis-Hastings method. The diffusion steps are carried out by sampling conditioned on the 'best so far', making it local to the 'best so far' sample, while jump steps remain unconditioned and span across the correspondence and motion space according to a similarity based proposal distribution making large movements. We advance the theory in three novel ways. First, a similarity based prior proposal distribution which guide jump steps. Second, JFD based likelihoods which guide diffusion steps allowing more focused correspondence establishment while searching for epipolar geometry. Third, a measure of degeneracy that allows to rule out degenerate configurations. The jump diffusion framework thus defined allows handling over 90% outliers even in cases where the number of inliers is very few. Practically, the advancement lies in higher precision and accuracy that has been detailed in this work by comparisons. In this work, BLOGS is compared with LO-RANSAC, NAPSAC, MAPSAC and BEEM algorithm, which are the current state of the art competing methods, on a dataset that has significantly more change in baseline, rotation, and scale than those used in the state of the art. Performance of these algorithms and BLOGS are quantitatively benchmark for a comparison by estimating the error in the epipolar geometry given by root mean Sampson's distance from manually specified corresponding point pairs which serve as a ground truth. Not just is BLOGS able to tolerate very high outlier rates, but also gives result of similar quality in 10 times lesser number of iterations than the most competitive among the compared algorithms.

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