A robust Hough Transform based method for direction detection and its application

Dong, W; He, X; Hintz, T

A robust Hough Transform based method for direction detection and its application

Dong, W He, X

Hintz, T

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007, 2007, pp. 439 - 443
Issue Date:: 2007-12-01

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Field	Value	Language
dc.contributor.author	Dong, W	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Hintz, T	en_US
dc.date.issued	2007-12-01	en_US
dc.identifier.citation	Proceedings of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007, 2007, pp. 439 - 443	en_US
dc.identifier.isbn	9781601320438	en_US
dc.identifier.uri	http://hdl.handle.net/10453/6961
dc.description.abstract	Hough Transform (HT) is a widely used method for finding lines, circles and other image features by projecting images onto a parametric space to simplify the feature detection. Direction detection has played an important role in many applications. Various approaches including HT-based methods have been developed for direction detection. Most HT-based methods try first to enhance image line features of an image, and then map the enhanced features onto Hough space to find line(s). In this paper, a new robust method which is based on Hough Transform is proposed. Standard Deviation as a cost function is applied along every angle index in Hough space and it transfers the two-dimensional Hough space into a one-dimensional curve which can reveal the overall directional information in the original image. Its full utilization of statistic information inherent within Hough space makes it sensitive to feature direction and robust to interference caused by bias in Hough space.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0451666
dc.relation.ispartof	Proceedings of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007	en_US
dc.title	A robust Hough Transform based method for direction detection and its application	en_US
dc.type	Conference Proceeding
utslib.for	1702 Cognitive Sciences	en_US
dc.location.activity	Las Vegas	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

Hough Transform (HT) is a widely used method for finding lines, circles and other image features by projecting images onto a parametric space to simplify the feature detection. Direction detection has played an important role in many applications. Various approaches including HT-based methods have been developed for direction detection. Most HT-based methods try first to enhance image line features of an image, and then map the enhanced features onto Hough space to find line(s). In this paper, a new robust method which is based on Hough Transform is proposed. Standard Deviation as a cost function is applied along every angle index in Hough space and it transfers the two-dimensional Hough space into a one-dimensional curve which can reveal the overall directional information in the original image. Its full utilization of statistic information inherent within Hough space makes it sensitive to feature direction and robust to interference caused by bias in Hough space.

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http://hdl.handle.net/10453/6961