Exploiting automatic image segmentation to human detection and depth estimation

Tseng, HC; Shyu, JJ; Chang, JY; Lin, CT

Exploiting automatic image segmentation to human detection and depth estimation

Tseng, HC Shyu, JJ Chang, JY Lin, CT

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Publication Type:: Conference Proceeding
Citation:: IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIMSIVP 2011: 2011 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, 2011, pp. 19 - 25
Issue Date:: 2011-08-10

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Field	Value	Language
dc.contributor.author	Tseng, HC	en_US
dc.contributor.author	Shyu, JJ	en_US
dc.contributor.author	Chang, JY	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2011-08-10	en_US
dc.identifier.citation	IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIMSIVP 2011: 2011 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing, 2011, pp. 19 - 25	en_US
dc.identifier.isbn	9781424499151	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120598
dc.description.abstract	In this paper, we combine image segmentation techniques and face detection methods to extract the human from scenes. Firstly, skin regions are detected and an ellipse fitting method is employed to detect the face region and consequently locate the human position. Then we propose an improved automatic seeded region growing algorithm to segment the image. The initial seeds are generated automatically, and the remaining pixels are classified to the nearest region. After the region growing procedure, two neighboring regions with high similarity are merged. The human body is determined by confining semantic human body region in segmented regions, and those belonging to the human face and human body are merged afterward. Lastly, we will detect the human vertical y-coordinate values in the image, and the depths can then be estimated according to the depth look-up tables of the camera. © 2011 IEEE.	en_US
dc.relation.ispartof	IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIMSIVP 2011: 2011 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing	en_US
dc.relation.isbasedon	10.1109/CIMSIVP.2011.5949245	en_US
dc.title	Exploiting automatic image segmentation to human detection and depth estimation	en_US
dc.type	Conference Proceeding
utslib.for	0902 Automotive Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, we combine image segmentation techniques and face detection methods to extract the human from scenes. Firstly, skin regions are detected and an ellipse fitting method is employed to detect the face region and consequently locate the human position. Then we propose an improved automatic seeded region growing algorithm to segment the image. The initial seeds are generated automatically, and the remaining pixels are classified to the nearest region. After the region growing procedure, two neighboring regions with high similarity are merged. The human body is determined by confining semantic human body region in segmented regions, and those belonging to the human face and human body are merged afterward. Lastly, we will detect the human vertical y-coordinate values in the image, and the depths can then be estimated according to the depth look-up tables of the camera. © 2011 IEEE.

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http://hdl.handle.net/10453/120598