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Robert B. Fisher
Researcher at University of Edinburgh
Publications - 326
Citations - 13279
Robert B. Fisher is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Image segmentation & Pattern recognition (psychology). The author has an hindex of 49, co-authored 315 publications receiving 12239 citations. Previous affiliations of Robert B. Fisher include IBM & Eastman Kodak Company.
Papers
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Journal ArticleDOI
Direct least squares fitting of ellipses
TL;DR: This paper presents a new efficient method for fitting ellipses to scattered data that is ellipse-specific so that even bad data will always return an ellipso, and can be solved naturally by a generalized eigensystem.
Journal ArticleDOI
An experimental comparison of range image segmentation algorithms
Adam Hoover,G. Jean-Baptiste,Xiaoyi Jiang,Patrick J. Flynn,Horst Bunke,Dmitry B. Goldgof,Kevin W. Bowyer,D. W. Eggert,Andrew Fitzgibbon,Robert B. Fisher +9 more
TL;DR: A methodology for evaluating range image segmentation algorithms and four research groups have contributed to evaluate their own algorithm for segmenting a range image into planar patches.
Journal ArticleDOI
Estimating 3-D rigid body transformations: a comparison of four major algorithms
TL;DR: A comparative analysis of four popular and efficient algorithms, each of which computes the translational and rotational components of the transform in closed form, as the solution to a least squares formulation of the problem, indicates that under “ideal” data conditions certain distinctions in accuracy and stability can be seen.
Proceedings ArticleDOI
A buyer's guide to conic fitting
TL;DR: This paper evaluates several algorithms under various noise and degeneracy conditions, identifies the key parameters which affect sensitivity, and presents the results of comparative experiments which emphasize the algorithms' behaviours under common examples of degenerate data.
Journal ArticleDOI
Object-based visual attention for computer vision
Yaoru Sun,Robert B. Fisher +1 more
TL;DR: Two new mechanisms which direct visual attention in the proposed object-based visual attention system are object-driven as well as feature-driven, and the first mechanism computes the visual salience of objects and groupings and implements the hierarchical selectivity of attentional shifts.