Robust color segmentation through adaptive color distribution transformation

Color segmentation is typically the first step of vision processing for a robot operating in a color-coded environment, such as RoboCup soccer, and many object recognition modules rely on that. Although many approaches to color segmentation have been proposed, in the official games of the RoboCup Four Legged League manual calibration is still preferred by most of the teams. In this paper we present a method for color segmentation that is based on an adaptive transformation of the color distribution of the image: the transformation is dynamically computed depending on the current image (i.e., it adapts to condition changes) and then it is used for color segmentation with static thresholds. The method requires the setting of only a few parameters and has been proved to be very robust to noise and light variations, allowing for setting parameters only once when arriving at a competition site. The approach has been implemented on AIBO robots, extensively tested in our laboratory, and successfully experimented in the some of the games of the Four Legged League in RoboCup 2005. © Springer-Verlag Berlin Heidelberg 2007.