A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
International Journal of Computer Vision
Randomized Hough transform: improved ellipse detection with comparison
Pattern Recognition Letters
Fast and Accurate Robot Vision for Vision Based Motion
RoboCup 2000: Robot Soccer World Cup IV
Fast Image Segmentation, Object Recognition and Localization in a RoboCup Scenario
RoboCup-99: Robot Soccer World Cup III
Techniques for Obtaining Robust, Real-Time, Colour-Based Vision for Robotics
RoboCup-99: Robot Soccer World Cup III
A Segmentation System for Soccer Robot Based on Neural Networks
RoboCup-99: Robot Soccer World Cup III
Tracking and Segmenting People in Varying Lighting Conditions Using Colour
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
A fast model-based vision system for a robot soccer team
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Benchmarks for robotic soccer vision
Robot Soccer World Cup XV
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In order to detect objects using colour information, the mapping from points in colour space to the most likely object must be known. This work proposes an adaptive colour calibration based on the Bayes Theorem and chrominance histograms. Furthermore the object's shape is considered resulting in a more robust classification. A randomised hough transform is employed for the ball. The lines of the goals and flagposts are extracted by an orthogonal regression. Shape detection corrects over- and undersegmentations of the colour segmentation, thus enabling an update of the chrominance histograms. The entire algorithm, including a segmentation and a recalibration step, is robust enough to be used during a RoboCup game and runs in real-time.