E
Eitan Sharon
Researcher at Weizmann Institute of Science
Publications - 25
Citations - 2736
Eitan Sharon is an academic researcher from Weizmann Institute of Science. The author has contributed to research in topics: Segmentation-based object categorization & Scale-space segmentation. The author has an hindex of 19, co-authored 25 publications receiving 2658 citations. Previous affiliations of Eitan Sharon include University of California, Los Angeles & IEEE Computer Society.
Papers
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Journal ArticleDOI
Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification
TL;DR: In this paper, a Bayesian formulation for incorporating soft model assignments into the calculation of affinities is presented. And the resulting soft model assignment is integrated into the multilevel segmentation by weighted aggregation algorithm, and applied to the task of detecting and segmenting brain tumor and edema in multichannel magnetic resonance (MR) volumes.
Proceedings ArticleDOI
Combining Top-Down and Bottom-Up Segmentation
TL;DR: This work shows how to combine bottom-up and top-up approaches into a single figure-ground segmentation process that provides accurate delineation of object boundaries that cannot be achieved by either the top-down or bottom- up approach alone.
Proceedings ArticleDOI
Fast multiscale image segmentation
TL;DR: A fast, multiscale algorithm for image segmentation that uses modern numeric techniques to find an approximate solution to normalized cut measures in time that is linear in the size of the image with only a few dozen operations per pixel.
Journal ArticleDOI
Hierarchy and adaptivity in segmenting visual scenes
TL;DR: A new, highly efficient approach that determines all salient regions of an image and builds them into a hierarchical structure, derived from algebraic multigrid solvers for physical systems, and consists of fine-to-coarse pixel aggregation.
Journal ArticleDOI
Shape Representation and Classification Using the Poisson Equation
TL;DR: This work presents a novel approach that allows to reliably compute many useful properties of a silhouette that assigns, for every internal point of the silhouette, a value reflecting the mean time required for a random walk beginning at the point to hit the boundaries.