This paper develops a weakly supervised algorithm that learns to segment rigid multi-colored objects from a set of training images and key points. The approach uses congealing to learn a probabilistic spatial model of the multi-colored object class and graph-cut to separate the foreground from the background. The result is a novel approach which can segment heterogeneous objects, in contrast to other recent approaches which are better at segmenting uniform but possibly flexible objects.

A new RoboCup soccer league is being developed, focusing on human-robot interaction. In this league each team consists of both a human player, mounted on a Segway HT scooter, and a robotic version of the Segway; both human and robot players must cooperate to score goals. This paper details the design of our robotic Segway soccer brain-based device (SS-BBD). The SS-BBD control system is based on a large scale neural simulation, whose design is dictated by details from the published literature on vertebrate neuroanatomy, neurophysiology, and psychophysics. The physical device is completely autonomous, and possesses special manipulators for kicking and capturing a full-sized soccer ball. The SS-BBD uses visual and laser rangefinder information to recognize a variety of game related objects, which enables it to perform actions such as capturing the ball, kicking the ball to another player, shooting a goal, and maneuvering safely across the field. The SS-BBD can act autonomously or obey voice commands from the human player. This is an unprecedented level of human-robot teamwork on a soccer field, in that our players are not merely acting autonomously, but also communicate with each other and support each other on the field