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A robust and accumulator-free ellipse hough transform

Published: 10 October 2004 Publication History

Abstract

The ellipse Hough transform (EHT) is a widely-used technique. Most of the previous modifications to the standard EHT improved either the voting procedure that computes the absolute measure function (AMF) or the peak detection of the AMF. However, existing EHTs are not robust for detecting partial slightly-oblique ellipses. This paper presents a Robust and Accumulator-Free Ellipse Hough Transform (RAF-EHT), an improved EHT that is robust even for partial slightly-oblique ellipses. Our RAF-EHT is based on two main ideas, namely, (1) an improved measure function (IMF) for handling the partiality and the obliqueness of ellipses, (2) a new accumulator-free computation scheme for finding the top k peaks of the IMF, without complex peak detection. Experimental results show that the RAF-EHT is more robust than the existing EHTs in detecting the partial slightly-oblique ellipses. In addition, the RAF-EHT needs only a little memory because it is accumulator-free.

References

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cover image ACM Conferences
MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia
October 2004
1028 pages
ISBN:1581138938
DOI:10.1145/1027527
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 October 2004

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Author Tags

  1. accumulator-free
  2. ellipse
  3. hough transform
  4. measure function

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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  • (2023)Track Detection Algorithm Based on Trace Correlation Using Hough TransformJournal of the Russian Universities. Radioelectronics10.32603/1993-8985-2023-26-2-65-7726:2(65-77)Online publication date: 2-May-2023
  • (2023)Athlete Action Recognition in Sports Video: A SurveyInternational Virtual Conference on Industry 4.010.1007/978-981-19-9989-5_14(153-170)Online publication date: 1-Apr-2023
  • (2017)Soccer Video Event Annotation by Synchronization of Attack–Defense Clips and Match Reports With Coarse-Grained Time InformationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2016.251528027:5(1104-1117)Online publication date: 1-May-2017
  • (2017)Ball tracking in sports: a surveyArtificial Intelligence Review10.1007/s10462-017-9582-2Online publication date: 16-Oct-2017
  • (2016)An Accurate and Robust Artificial Marker Based on Cyclic CodesIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2016.251902438:12(2359-2373)Online publication date: 1-Dec-2016
  • (2014)Event boundary determination based on attack-defense transition analysis in soccer video2014 19th International Conference on Digital Signal Processing10.1109/ICDSP.2014.6900680(321-326)Online publication date: Aug-2014
  • (2011)RUNE-TagProceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition10.1109/CVPR.2011.5995544(113-120)Online publication date: 20-Jun-2011
  • (2009)Lift-button detection and recognition for service robot in buildingsProceedings of the 16th IEEE international conference on Image processing10.5555/1818719.1818823(313-316)Online publication date: 7-Nov-2009
  • (2007)A fast and robust ellipse detection algorithm based on pseudo-random sample consensusProceedings of the 12th international conference on Computer analysis of images and patterns10.5555/1770904.1770996(669-676)Online publication date: 27-Aug-2007
  • (2007)A Fast and Robust Ellipse Detection Algorithm Based on Pseudo-random Sample ConsensusComputer Analysis of Images and Patterns10.1007/978-3-540-74272-2_83(669-676)Online publication date: 2007
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