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ROCIT : a visual object recognition algorithm based on a rank-order coding scheme.
Author(s) -
Antonio Gonzáles,
Paul Reeves,
John W. Jones,
Benjamin Farkas
Publication year - 2004
Publication title -
osti oai (u.s. department of energy office of scientific and technical information)
Language(s) - English
Resource type - Reports
DOI - 10.2172/919190
Subject(s) - computer science , coding (social sciences) , benchmark (surveying) , fidelity , artificial intelligence , cognitive neuroscience of visual object recognition , algorithm , implementation , artificial neural network , visual cortex , pattern recognition (psychology) , object (grammar) , computer vision , mathematics , neuroscience , biology , telecommunications , statistics , geodesy , programming language , geography
This document describes ROCIT, a neural-inspired object recognition algorithm based on a rank-order coding scheme that uses a light-weight neuron model. ROCIT coarsely simulates a subset of the human ventral visual stream from the retina through the inferior temporal cortex. It was designed to provide an extensible baseline from which to improve the fidelity of the ventral stream model and explore the engineering potential of rank order coding with respect to object recognition. This report describes the baseline algorithm, the model's neural network architecture, the theoretical basis for the approach, and reviews the history of similar implementations. Illustrative results are used to clarify algorithm details. A formal benchmark to the 1998 FERET fafc test shows above average performance, which is encouraging. The report concludes with a brief review of potential algorithmic extensions for obtaining scale and rotational invariance

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