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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Gönner, C., Rous, M., & Kraiss, K. F. (2005). Real-time adaptive colour segmentation for the RoboCup middle size league. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3276, pp. 402–409). Springer Verlag. https://doi.org/10.1007/978-3-540-32256-6_33
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