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2008

Object recognition using a generalized robust invariant feature and Gestalt's law of proximity and similarity

8 years 8 months ago
Object recognition using a generalized robust invariant feature and Gestalt's law of proximity and similarity
In this paper, we propose a new context-based method for object recognition. We first introduce a neuro-physiologically motivated visual part detector. We found that the optimal form of the visual part detector is a combination of a radial symmetry detector and a corner-like structure detector. A general context descriptor, named G-RIF (generalized-robust invariant feature), is then proposed, which encodes edge orientation, edge density and hue information in a unified form. Finally, a context-based voting scheme is proposed. This proposed method is inspired by the function of the human visual system, called figure-ground discrimination. We use the proximity and similarity between features to support each other. The contextual feature descriptor and contextual voting method, which use contextual information, enhance the recognition performance enormously in severely cluttered environments. 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Sungho Kim, Kuk-Jin Yoon, In-So Kweon
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2008
Where PR
Authors Sungho Kim, Kuk-Jin Yoon, In-So Kweon
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