Toward RoboCup without Color Labeling

Hence, no training phase is needed. The local statistics define an with white lines; goals are blue and yellow; and expectation of "how the two sides of the curve robots are black with light blue or magenta might look." Second, refine the estimation of model parameters These stringent rules allow for simple mechanisms by (1) updating the mean of the estimation for object detection and recognition: in a maximum a posteriori step such that Segment the captured image into blobs of the the vicinity of the curve matches the expectation same color and interpret these blobs. To the defined by the local statistics and (2) updating best of our knowledge, all autonomous robot the covariance of the estimation based on soccer teams with vision-based perception apply the Hessian of the resulting objective function. However, because The two steps are repeated until there is no the RoboCup committee is planning to significant change in the estimated Gaussian make the rules more realistic, these objectrecognition distribution.