Enhancing the Randomized Hough Transform with k-means clustering to detect mutually-occluded ellipses

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

In the attempts to resolve the problem of ellipse detection, the Randomized Hough Transform (RHT) serves as a powerful variant of the standard Hough transform that exploits the geometric properties of ellipses in order to speed up the detection process. Despite its simplicity and efficiency, the RHT performs poorly if the target ellipses are overlapped (or mutually-occluded) with each other. We present a novel method that utilizes k-means clustering to boost the performance of the RHT in detecting mutually-occluded ellipses, and test its effectiveness for both synthetic and real-world images. However, as a result of using k-means clustering, this method is susceptible to being stuck at a local optima.

Original languageEnglish (US)
Title of host publication2011 19th Mediterranean Conference on Control and Automation, MED 2011
Pages327-332
Number of pages6
DOIs
StatePublished - 2011
Event2011 19th Mediterranean Conference on Control and Automation, MED 2011 - Corfu, Greece
Duration: Jun 20 2011Jun 23 2011

Publication series

Name2011 19th Mediterranean Conference on Control and Automation, MED 2011

Other

Other2011 19th Mediterranean Conference on Control and Automation, MED 2011
CountryGreece
CityCorfu
Period6/20/116/23/11

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