Sequential simulated annealing system for pattern detection

Kou-Yuan Huang*, Ying Liang Chou

*Corresponding author for this work

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

Abstract

Asequential system is proposed by using simulated annealing for the detection of lines, circles, ellipses, and hyperbolas in image. The sequential detection procedures are type by type and patterns by patterns. The equation of ellipse and hyperbola is defined under translation and rotation. The distance from all points to all patterns is defined as the error. Using simulated annealing for parameter detection can search a set of parameter vectors for the global minimal error. We also propose synchronous simulated annealing detection system to compare with sequential system. In the experiments, using the sequential system, the result of a large number of simulated and real image patterns detection is better than that of using the synchronous system.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing - PCM 2008 - 9th Pacific Rim Conference on Multimedia, Proceedings
Pages338-347
Number of pages10
DOIs
StatePublished - 1 Dec 2008
Event9th Pacific Rim Conference on Multimedia, PCM 2008 - Tainan, Taiwan
Duration: 9 Dec 200813 Dec 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5353 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th Pacific Rim Conference on Multimedia, PCM 2008
CountryTaiwan
CityTainan
Period9/12/0813/12/08

Keywords

  • Image detection
  • Pattern recognition
  • Simulated annealing

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    Huang, K-Y., & Chou, Y. L. (2008). Sequential simulated annealing system for pattern detection. In Advances in Multimedia Information Processing - PCM 2008 - 9th Pacific Rim Conference on Multimedia, Proceedings (pp. 338-347). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5353 LNCS). https://doi.org/10.1007/978-3-540-89796-5_35