Crossed-line segmentation for low-level vision

John Atkinson, Claudio Castro

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


This work describes a new segmentation method for robotic soccer applications. The approach called crossed-line segmentation is based on the combination of region classification and a border detector which meet homogeneity criteria of medians. Experiments suggest that the method outperforms traditional procedure in terms of smoothing and segmentation accuracy. Furthermore, existing noise in the images is also observed to be reduced without missing the objects' borders.

Original languageEnglish
Title of host publicationRoboCup 2007
Subtitle of host publicationRobot Soccer World Cup XI
Number of pages8
StatePublished - 2008
Externally publishedYes
Event11th RoboCup International Symposium, RoboCup 2007 - Atlanta, GA, United States
Duration: 9 Jul 200710 Jul 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5001 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th RoboCup International Symposium, RoboCup 2007
Country/TerritoryUnited States
CityAtlanta, GA


  • BLOBs
  • Color-based segmentation
  • Robotic vision


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