Categorical color projection for robot road following

This paper shows that categorical color developed in Yue Du (1994) is a good representation of color space for some robot vision tasks. The authors use SCARF as a test case to compare `equivalent´ RGB, categorical color, and intensity robot vision algorithms on a difficult robot road following task. SCARF uses a full three-dimensional representation of color space to formulate road and off-road colors. Performing categorical color or intensity projection, the authors can model the road and off-road colors as single dimensional probability density functions. This greatly reduces the time to process the color image. Moreover, the authors show that categorical color is a good representation in that it outperforms an equivalent intensity algorithm on the robot tasks. Categorical color, while it cannot outperform the original RGB algorithm, performs well enough to be used in this particular robot application. Its effectiveness is verified by many experiments on difficult road image sequences