An efficient segmentation technique for known touching objects using a genetic algorithm approach

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

2 Citations (Scopus)

Abstract

This paper presents a genetic algorithm (GA) based segmentation technique that can separate two touching objects intended for an automatic recognition of plastic bottles moving on a conveyor belt. The proposed method is based on the possibility to separate the two objects by means of a straight line, whose position is determined by a GA. Extensive testing shows that the proposed method is fast and yields high success rate of correct segmentation with only a limited number of both chromosomes and iterations.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages786-790
Number of pages5
Volume4830 LNAI
Publication statusPublished - 2007
Event20th Australian Joint Conference on Artificial Intelligence, AI 2007 - Gold Coast
Duration: 2 Dec 20076 Dec 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4830 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other20th Australian Joint Conference on Artificial Intelligence, AI 2007
CityGold Coast
Period2/12/076/12/07

Fingerprint

Segmentation
Genetic algorithms
Plastic bottles
Genetic Algorithm
Chromosomes
Straight Line
Plastics
Chromosome
Iteration
Testing
Object

Keywords

  • Genetic algorithm (GA)
  • Segmentation

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Scavino, E., Abd. Wahab, D., Basri, H., Mustafa, M. M., & Hussain, A. (2007). An efficient segmentation technique for known touching objects using a genetic algorithm approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4830 LNAI, pp. 786-790). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4830 LNAI).

An efficient segmentation technique for known touching objects using a genetic algorithm approach. / Scavino, Edgar; Abd. Wahab, Dzuraidah; Basri, Hassan; Mustafa, Mohd. Marzuki; Hussain, Aini.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4830 LNAI 2007. p. 786-790 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4830 LNAI).

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

Scavino, E, Abd. Wahab, D, Basri, H, Mustafa, MM & Hussain, A 2007, An efficient segmentation technique for known touching objects using a genetic algorithm approach. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4830 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4830 LNAI, pp. 786-790, 20th Australian Joint Conference on Artificial Intelligence, AI 2007, Gold Coast, 2/12/07.
Scavino E, Abd. Wahab D, Basri H, Mustafa MM, Hussain A. An efficient segmentation technique for known touching objects using a genetic algorithm approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4830 LNAI. 2007. p. 786-790. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Scavino, Edgar ; Abd. Wahab, Dzuraidah ; Basri, Hassan ; Mustafa, Mohd. Marzuki ; Hussain, Aini. / An efficient segmentation technique for known touching objects using a genetic algorithm approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4830 LNAI 2007. pp. 786-790 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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