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dc.contributor.authorOlson, Clark F.en_US
dc.date.accessioned2007-04-23T18:02:27Z
dc.date.available2007-04-23T18:02:27Z
dc.date.issued1995-05en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR95-1516en_US
dc.identifier.urihttps://hdl.handle.net/1813/7173
dc.description.abstractThis paper describes techniques to perform fast and accurate curve detection using a variant of the Hough transform. It is shown that the Hough transform can be decomposed into many small subproble ms, where each subproblem considers only curves that pass through some subset of the points. These curves correspond to a manifold in the parameter space. This property allows the effects of localization error to be modeled more accurately than previous systems. The additional use of randomization techniques leads to efficient algorithms. The time required by this method is $O(n)$, where $n$ is the number of edge poin ts in the image, if we are only required to find curves that are significant wit h respect to the complexity of the image. Results are given showing the detecti on of lines and circles in real images.en_US
dc.format.extent1285626 bytes
dc.format.extent4404149 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleImproved Curve Detection Through Decomposition of the Hough Transformen_US
dc.typetechnical reporten_US


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