Computational Cognitive Color Perception

Ozer Ciftcioglu, Michael Bittermann

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
135 Downloads (Pure)

Abstract

Comprehension of aesthetical color characteristics based on a computational model of visual perception and color cognition are presented. The computational comprehension is manifested by the machine’s capability of instantly assigning appropriate colors to the objects perceived. They form a scene with aesthetically pleasing characteristics. The present approach to computational cognition is principally the same as contrived earlier [1]. This work distinguishes itself from the earlier work through the involvement of color differences. The color difference computations are carried out based on a standard human color observer model. The color difference information is
combined with geometric perception information using the method of fuzzy neural tree based on likelihood. The study exemplifies the suitability of the computational cognition for modeling cognition phenomenon. Cognitive color perception in computational form has generic relevance to applications involving human-like aesthetical appreciation, as is the case in building architecture, for instance and other design tasks.
Original languageEnglish
Title of host publicationProceedings 2016 IEEE Congress on Evolutionary Computation (CEC)
PublisherIEEE
Pages2262-2271
ISBN (Print)978-150900622-9
DOIs
Publication statusPublished - 2016
Event2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Conference

Conference2016 IEEE Congress on Evolutionary Computation, CEC 2016
Abbreviated titleCEC 2016
CountryCanada
CityVancouver
Period24/07/1629/07/16

Keywords

  • visual perception
  • color difference
  • cognitive computing
  • genetic algorithm
  • fuzzy neural tree
  • auto-association

Fingerprint Dive into the research topics of 'Computational Cognitive Color Perception'. Together they form a unique fingerprint.

Cite this