Biologically motivated perceptual feature: Generalized robust invariant feature

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In this paper, we present a new, biologically inspired perceptual feature to solve the selectivity and invariance issue in object recognition. Based on the recent findings in neuronal and cognitive mechanisms in human visual systems, we develop a computationally efficient model. An effective form of a visual part detector combines a radial symmetry detector with a corner-like structure detector. A general context descriptor encodes edge orientation, edge density, and hue information using a localized receptive field histogram. We compare the proposed perceptual feature (C-RIF: generalized robust invariant feature) with the state-of-the-art feature, SIFT, for feature-based object recognition. The experimental results validate the robustness of the proposed perceptual feature in object recognition.
Publisher
SPRINGER-VERLAG BERLIN
Issue Date
2006
Language
English
Article Type
Article; Proceedings Paper
Keywords

INTEREST POINT DETECTORS; RECOGNITION; SCALE; V4

Citation

COMPUTER VISION - ACCV 2006, PT II BOOK SERIES: LECTURE NOTES IN COMPUTER SCIENCE, v.3852, pp.305 - 314

ISSN
0302-9743
URI
http://hdl.handle.net/10203/90926
Appears in Collection
EE-Journal Papers(저널논문)
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