Analysis of a biologically-inspired system for real-time object recognition

We present a biologically-inspired system for real-time, feed-forward object recognition in cluttered scenes. Our system utilizes a vocabulary of very sparse features that are shared between and within different object m
We present a biologically-inspired system for real-time, feed-forward object recognition in cluttered scenes. Our system utilizes a vocabulary of very sparse features that are shared between and within different object models. To detect objects in a novel scene, these features are located in the image, and each detected feature votes for all objects that are consistent with its presence. Due to the sharing of features between object models our approach is more scalable to large object databases than traditional methods. To demonstrate the utility of this approach, we train our system to recognize any of 50 objects in everyday cluttered scenes with substantial occlusion. Without further optimization we also demonstrate near-perfect recognition on a standard 3-D recognition problem. Our system has an interpretation as a sparsely connected feed-forward neural network, making it a viable model for fast, feed-forward object recognition in the primate visual system.
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Metadaten
Author:Erik Murphy-Chutorian, Sarah Aboutalib, Jochen Triesch
URN:urn:nbn:de:hebis:30-26676
Document Type:Article
Language:English
Date of Publication (online):2006/05/12
Year of first Publication:2005
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2006/05/12
Source:Cognitive Science Online, 3.2, pp. 1-15, http://cogsci-online.ucsd.edu/3/3-3.pdf
HeBIS PPN:265244404
Institutes:Informatik
Dewey Decimal Classification:004 Datenverarbeitung; Informatik
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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