Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/91593
Title: A size and position invariant event-based human posture recognition algorithm
Authors: Chen, Shoushun
Folowosele, Fopefolu
Kim, Dongsoo
Vogelstein, R. Jacob
Etienne-Cummings, Ralph
Culurciello, Eugenio
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2008
Source: 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).
Abstract: 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.
URI: https://hdl.handle.net/10356/91593
http://hdl.handle.net/10220/6372
DOI: http://dx.doi.org/10.1109/BIOCAS.2008.4696930
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Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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