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Title: Efficient feedforward categorization of objects and human postures with address-event image sensors
Authors: Chen, Shoushun
Akselrod, Polina
Zhao, Bo
Carrasco, Jose Antonio Perez
Linares-Barranco, Bernabe
Culurciello, Eugenio
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2012
Source: Chen, S., Akseirod, P., Zhao, B., Carrasco, J. A. P., Linares-Barranco, B., & Culurciello, E. (2012). Efficient feedforward categorization of objects and human postures with address-event image sensors. IEEE transactions on pattern analysis and machine intelligence, 34(2), 302-314.
Series/Report no.: IEEE transactions on pattern analysis and machine intelligence
Abstract: This paper proposes an algorithm for feedforward categorization of objects and, in particular, human postures in real-time video sequences from address-event temporal-difference image sensors. The system employs an innovative combination of event-based hardware and bio-inspired software architecture. An event-based temporal difference image sensor is used to provide input video sequences, while a software module extracts size and position invariant line features inspired by models of the primate visual cortex. The detected line features are organized into vectorial segments. After feature extraction, a modified line segment Hausdorff-distance classifier combined with on-the-fly cluster-based size and position invariant categorization. The system can achieve about 90 percent average success rate in the categorization of human postures, while using only a small number of training samples. Compared to state-of-the-art bio-inspired categorization methods, the proposed algorithm requires less hardware resource, reduces the computation complexity by at least five times, and is an ideal candidate for hardware implementation with event-based circuits.
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2011.120
Rights: © 2012 IEEE
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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