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.
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Kim, S., Kweon, I.S. (2006). Biologically Motivated Perceptual Feature: Generalized Robust Invariant Feature. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_31
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DOI: https://doi.org/10.1007/11612704_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31244-4
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