MLESAC: a new robust estimator with application to estimating image geometry by Torr, PHS, Zisserman, A

“…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. …”

IMPSAC: synthesis of importance sampling and random sample consensus by Torr, PHS, Davidson, C

“…It is shown that the method is superior to previous single resolution RANSAC-style feature matchers.…”

An integrated Bayesian approach to layer extraction from image sequences by Torr, PHS, Szeliski, R, Anandan, P

“…In order to achieve the optimization, a Bayesian version of RANSAC is developed with which to initialize the segmentation. …”

Efficient online structured output learning for keypoint-based object tracking by Hare, S, Saffari, A, Torr, PHS

“…These approaches often model an object as a collection of keypoints and associated descriptors, and detection then involves first constructing a set of correspondences between object and image keypoints via descriptor matching, and subsequently using these correspondences as input to a robust geometric estimation algorithm such as RANSAC to find the transformation of the object in the image. …”

Robust detection of degenerate configurations while estimating the fundamental matrix by Torr, PHS, Zisserman, A, Maybank, SJ

“…The method is called PLUNDER-DL and is a generalization of the robust estimator RANSAC.</p> <p>The method is evaluated on many differing pairs of real images. …”

Real-time RGB-D camera pose estimation in novel scenes using a relocalisation cascade by Cavallari, T, Golodetz, S, Lord, NA, Valentin, J, Prisacariu, VA, Di Stefano, L, Torr, PHS

“…To achieve this, we make several changes to the original approach: (i) instead of simply accepting the camera pose hypothesis produced by RANSAC without question, we make it possible to score the final few hypotheses it considers using a geometric approach and select the most promising one; (ii) we chain several instantiations of our relocaliser (with different parameter settings) together in a cascade, allowing us to try faster but less accurate relocalisation first, only falling back to slower, more accurate relocalisation as necessary; and (iii) we tune the parameters of our cascade, and the individual relocalisers it contains, to achieve effective overall performance. …”