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Comparison of Color Identification on Soccer Robot using Color Filtering, k-NN and Naive Bayes

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

Abstract

Accuracy level and computation time to identify color object are two important issues in designing vision of soccer robot. In this research three classification methods were used to solve those problems, i.e. Color Filtering, Naïve Bayes and $k$-Nearest Neighbor (k-NN). These methods were used for color-based image segmentation, object detection, center point coordinate and object distance measurement with scanning and tracking action. The result shows the average error of 3.6% by means of Naïve Bayes classification within distance estimation of 10 cm up to 360 cm. The fastest computation time was required for the object color identification using Color Filtering, i.e. 0.097 s. The average error and computation time resulted by k-NN method were 7.19% and 0.27 s, respectively.

Original languageEnglish
Title of host publication2018 2nd International Conference on Applied Electromagnetic Technology, AEMT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages57-60
Number of pages4
ISBN (Electronic)9781538626085
DOIs
Publication statusPublished - 10 Dec 2018
Event2018 2nd International Conference on Applied Electromagnetic Technology, AEMT 2018 - Lombok, Indonesia
Duration: 9 Apr 201812 Apr 2018

Publication series

Name2018 2nd International Conference on Applied Electromagnetic Technology, AEMT 2018

Conference

Conference2018 2nd International Conference on Applied Electromagnetic Technology, AEMT 2018
Country/TerritoryIndonesia
CityLombok
Period9/04/1812/04/18

Keywords

  • Color Filtering
  • k-Nearest Neighbor (k-NN)
  • Naive Bayes
  • Soccer robot

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