Accession Number : ADA454604


Title :   Object Recognition with Features Inspired by Visual Cortex


Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE DEPT OF BRAIN AND COGNITIVE SCIENCES


Personal Author(s) : Serre, Thomas ; Wolf, Lior ; Poggio, Tomaso


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a454604.pdf


Report Date : Jan 2006


Pagination or Media Count : 8


Abstract : We introduce a novel set of features for robust object recognition. Each element of this set is a complex feature obtained by combining position- and scale-tolerant edge-detectors over neighboring positions and multiple orientations. Our system's architecture is motivated by a quantitative model of visual cortex. We show that our approach exhibits excellent recognition performance and outperforms several state-of-the-art systems on a variety of image datasets including many different object categories. We also demonstrate that our system is able to learn from very few examples. The performance of the approach constitutes a suggestive plausibility proof for a class of feedforward models of object recognition in cortex.


Descriptors :   *COMPUTER VISION , EDGES , ARCHITECTURE , VISUAL CORTEX , PATTERN RECOGNITION


Subject Categories : Cybernetics


Distribution Statement : APPROVED FOR PUBLIC RELEASE