A size and position invariant event-based human posture recognition algorithm

In this paper we report a size and position invariant human posture recognition algorithm. The algorithm employs a simplified line segment Hausdorff distance classification and uses projection histograms to achieve size and position invariance....

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Main Authors:	Chen, Shoushun, Folowosele, Fopefolu, Kim, Dongsoo, Vogelstein, R. Jacob, Etienne-Cummings, Ralph, Culurciello, Eugenio
Other Authors:	School of Electrical and Electronic Engineering
Format:	Conference or Workshop Item
Language:	English
Published:	2010
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Online Access:	https://hdl.handle.net/10356/91593 http://hdl.handle.net/10220/6372
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-915932020-03-07T13:24:46Z A size and position invariant event-based human posture recognition algorithm Chen, Shoushun Folowosele, Fopefolu Kim, Dongsoo Vogelstein, R. Jacob Etienne-Cummings, Ralph Culurciello, Eugenio School of Electrical and Electronic Engineering IEEE Biomedical Circuits and Systems Conference (2008 : Baltimore, Maryland, US) DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems In this paper we report a size and position invariant human posture recognition algorithm. The algorithm employs a simplified line segment Hausdorff distance classification and uses projection histograms to achieve size and position invariance. Compared to other existing method utilizing line segment Hausdorff distance, the proposed algorithm reduces the computation complexity by 36000 times, for our test images. Combining bioinspired event-based image acquisition and hardware friendly feature extraction and classification algorithm will lead to a promising technology for use in wireless sensor network. Published version 2010-08-31T01:07:41Z 2019-12-06T18:08:33Z 2010-08-31T01:07:41Z 2019-12-06T18:08:33Z 2008 2008 Conference Paper Chen, S., Folowosele, F., Kim, D., Vogelstein, R. J., Etienne-Cummings, R., & Culurciello, E. (2008). A size and position invariant event-based human posture recognition algorithm. IEEE Biomedical Circuits and Systems Conference: Baltimore,MD,USA, (pp.285-288). https://hdl.handle.net/10356/91593 http://hdl.handle.net/10220/6372 10.1109/BIOCAS.2008.4696930 en © 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. http://www.ieee.org/portal/site This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. 4 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Chen, Shoushun Folowosele, Fopefolu Kim, Dongsoo Vogelstein, R. Jacob Etienne-Cummings, Ralph Culurciello, Eugenio A size and position invariant event-based human posture recognition algorithm
description	In this paper we report a size and position invariant human posture recognition algorithm. The algorithm employs a simplified line segment Hausdorff distance classification and uses projection histograms to achieve size and position invariance. Compared to other existing method utilizing line segment Hausdorff distance, the proposed algorithm reduces the computation complexity by 36000 times, for our test images. Combining bioinspired event-based image acquisition and hardware friendly feature extraction and classification algorithm will lead to a promising technology for use in wireless sensor network.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Chen, Shoushun Folowosele, Fopefolu Kim, Dongsoo Vogelstein, R. Jacob Etienne-Cummings, Ralph Culurciello, Eugenio
format	Conference or Workshop Item
author	Chen, Shoushun Folowosele, Fopefolu Kim, Dongsoo Vogelstein, R. Jacob Etienne-Cummings, Ralph Culurciello, Eugenio
author_sort	Chen, Shoushun
title	A size and position invariant event-based human posture recognition algorithm
title_short	A size and position invariant event-based human posture recognition algorithm
title_full	A size and position invariant event-based human posture recognition algorithm
title_fullStr	A size and position invariant event-based human posture recognition algorithm
title_full_unstemmed	A size and position invariant event-based human posture recognition algorithm
title_sort	size and position invariant event-based human posture recognition algorithm
publishDate	2010
url	https://hdl.handle.net/10356/91593 http://hdl.handle.net/10220/6372
_version_	1681048620430786560

A size and position invariant event-based human posture recognition algorithm

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