Biologically motivated perceptual feature: generalized robust invariant feature

Authors:
Sungho Kim;In So Kweon
Affiliations:
Dept. of EECS, Korea Advanced Institute of Science and Technology, Daejeon, Korea;Dept. of EECS, Korea Advanced Institute of Science and Technology, Daejeon, Korea
Venue:
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Year:
2006

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Abstract

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 (G-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.