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dc.contributor.authorCofer, Rufus H.
dc.contributor.authorKozaitis, S.P.
dc.contributor.authorCha, Jihun
dc.date.accessioned2017-10-02T20:26:48Z
dc.date.available2017-10-02T20:26:48Z
dc.date.issued2003-11-26
dc.identifier.citationCofer, R. H., Kozaitis, S. P., & Cha, J. (2003). Extended hough methodology for 3-D feature detection. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 5243 158-164en_US
dc.identifier.urihttp://hdl.handle.net/11141/1682
dc.descriptionAutomated feature extraction, Hough transform, Image exploitation, Pattern recognitionen_US
dc.description.abstractIn an effort to make automatically detect image features for pattern recognition, we described a 3-dimensional (3-D) Hough transform. We describe two interlocking theoretical extensions to greatly enhance the Hough transform's ability to handle finite lineal features and allow directed search for various features while balancing memory and computational complexity. We computed the 2-D Hough transform of 1-D slices of an image which results in a 2-D to 3-D transform. Features such as line segments will cluster in a particular location so that both line orientation and spatial extent can be determined. This approach allows the Hough transform to be more widely applied in pattern recognition including 3-D features.en_US
dc.language.isoen_USen_US
dc.rightsThis published article is made available in accordance with publishers policy. It may be subject to U.S. copyright law.en_US
dc.rights.urihttp://spie.org/publications/journals/guidelines-for-authors#Terms_of_Useen_US
dc.titleExtended Hough methodology for 3-D feature detectionen_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.511256


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