MLESAC: A New Robust Estimator with Application to Estimating Image Geometry

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MLESAC: A New Robust Estimator with Application to Estimating Image Geometry (2000)

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by P. H. S. Torr , A. Zisserman

Venue:	Computer Vision and Image Understanding
Citations:	362 - 10 self

BibTeX

@ARTICLE{Torr00mlesac:a,
    author = {P. H. S. Torr and A. Zisserman},
    title = {MLESAC: A New Robust Estimator with Application to Estimating Image Geometry},
    journal = {Computer Vision and Image Understanding},
    year = {2000},
    volume = {78},
    pages = {2000}
}

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Abstract

A new method is presented for robustly estimating multiple view relations from point correspondences. The method comprises two parts. The first is a new robust estimator MLESAC which is a generalization of the RANSAC estimator. It adopts the same sampling strategy as RANSAC to generate putative solutions, but chooses the solution that maximizes the likelihood rather than just the number of inliers. The second part of the algorithm is a general purpose method for automatically parameterizing these relations, using the output of MLESAC. A difficulty with multiview image relations is that there are often nonlinear constraints between the parameters, making optimization a difficult task. The parameterization method overcomes the difficulty of nonlinear constraints and conducts a constrained optimization. The method is general and its use is illustrated for the estimation of fundamental matrices, image–image homographies, and quadratic transformations. Results are given for both synthetic and real images. It is demonstrated that the method gives results equal or superior to those of previous approaches. c ○ 2000 Academic Press 1.

Keyphrases

new robust estimator estimating image geometry nonlinear constraint second part real image new method ransac estimator parameterization method constrained optimization difficult task general purpose method quadratic transformation academic press point correspondence image image homographies multiview image relation fundamental matrix new robust estimator mlesac multiple view relation previous approach putative solution sampling strategy

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