Genetic algorithm-based solver for very large multiple Jigsaw puzzles of unknown dimensions and piece orientation

Dror Sholomon, Omid E. David, Nathan S. Netanyahu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

In this paper we propose the first genetic algorithm (GA)-based solver for jigsaw puzzles of unknown puzzle dimensions and unknown piece location and orientation. Our solver uses a novel crossover technique, and sets a new state-of-the-art in terms of the puzzle sizes solved and the accuracy obtained. The results are significantly improved, even when compared to previous solvers assuming known puzzle dimensions. Moreover, the solver successfully contends with a mixed bag of multiple puzzle pieces, assembling simultaneously all puzzles.

Original languageEnglish
Title of host publicationGECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery
Pages1191-1198
Number of pages8
ISBN (Print)9781450326629
DOIs
StatePublished - 2014
Event16th Genetic and Evolutionary Computation Conference, GECCO 2014 - Vancouver, BC, Canada
Duration: 12 Jul 201416 Jul 2014

Publication series

NameGECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference

Conference

Conference16th Genetic and Evolutionary Computation Conference, GECCO 2014
Country/TerritoryCanada
CityVancouver, BC
Period12/07/1416/07/14

Keywords

  • Computer vision
  • Genetic algorithms
  • Jigsaw puzzle
  • Recombination operators

Fingerprint

Dive into the research topics of 'Genetic algorithm-based solver for very large multiple Jigsaw puzzles of unknown dimensions and piece orientation'. Together they form a unique fingerprint.

Cite this