P
Peter Meer
Researcher at Rutgers University
Publications - 148
Citations - 34772
Peter Meer is an academic researcher from Rutgers University. The author has contributed to research in topics: Estimator & Image segmentation. The author has an hindex of 56, co-authored 148 publications receiving 33447 citations. Previous affiliations of Peter Meer include University of Maryland, College Park & Sogang University.
Papers
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Proceedings ArticleDOI
Nonlinear Mean Shift for Clustering over Analytic Manifolds
R. Subbarao,Peter Meer +1 more
TL;DR: The mean shift algorithm is generalized for clustering on matrix Lie groups and extended to a more general class of nonlinear spaces, the set of analytic manifolds, which is applied to a variety of robust motion segmentation problems and multibody factorization.
Proceedings ArticleDOI
A general method for Errors-in-Variables problems in computer vision
Bogdan Matei,Peter Meer +1 more
TL;DR: It is shown that the HEIV estimator can provide an accurate solution to most 3D vision estimation tasks, and illustrate its performance through two case studies: calibration and the estimation of the fundamental matrix.
Proceedings ArticleDOI
Learning on lie groups for invariant detection and tracking
TL;DR: This paper presents a novel learning based tracking model combined with object detection that can accurately detect objects in various poses, where the size of the search space is only a fraction compared to the existing object detection methods.
Proceedings ArticleDOI
Simultaneous multiple 3D motion estimation via mode finding on Lie groups
TL;DR: A new method to estimate multiple rigid motions from noisy 3D point correspondences in the presence of outliers is proposed and a mean shift algorithm which estimates modes of the sampled distribution using the Lie group structure of the rigid motions is developed.
Book ChapterDOI
Cell image segmentation for diagnostic pathology
Dorin Comaniciu,Peter Meer +1 more
TL;DR: This chapter reviews an efficient cell segmentation algorithm that detects clusters in the L*u*v color space and delineates their borders by employing the gradient ascent mean shift procedure.