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W.E.L. Grimson
Researcher at Massachusetts Institute of Technology
Publications - 90
Citations - 23861
W.E.L. Grimson is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Image segmentation & Cognitive neuroscience of visual object recognition. The author has an hindex of 49, co-authored 90 publications receiving 23346 citations. Previous affiliations of W.E.L. Grimson include University of Saskatchewan & ARCO.
Papers
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Proceedings ArticleDOI
Adaptive background mixture models for real-time tracking
Chris Stauffer,W.E.L. Grimson +1 more
TL;DR: This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes.
Journal ArticleDOI
Learning patterns of activity using real-time tracking
Chris Stauffer,W.E.L. Grimson +1 more
TL;DR: This paper focuses on motion tracking and shows how one can use observed motion to learn patterns of activity in a site and create a hierarchical binary-tree classification of the representations within a sequence.
Journal ArticleDOI
Adaptive segmentation of MRI data
TL;DR: Use of the expectation-maximization (EM) algorithm leads to a method that allows for more accurate segmentation of tissue types as well as better visualization of magnetic resonance imaging data, that has proven to be effective in a study that includes more than 1000 brain scans.
Proceedings ArticleDOI
Statistical shape influence in geodesic active contours
TL;DR: A novel method of incorporating shape information into the image segmentation process is presented, which introduces a representation for deformable shapes and defines a probability distribution over the variances of a set of training shapes.
Journal ArticleDOI
A shape-based approach to the segmentation of medical imagery using level sets
Andy Tsai,Anthony Yezzi,William M. Wells,Clare M. Tempany,D. Tucker,Ayres C. Fan,W.E.L. Grimson,Alan S. Willsky +7 more
TL;DR: A parametric model for an implicit representation of the segmenting curve is derived by applying principal component analysis to a collection of signed distance representations of the training data to minimize an objective function for segmentation.