Abstract
Recognition of relevant game field objects, such as the ball and landmarks, is usually based upon the application of a set of decision rules over candidate image regions. Rule selection and parameters tuning are often arbitrarily done. We propose a method for evolving the selection of these rules as well as their parameters with basis on real game field images, and a supervised learning approach. The learning approach is implemented using genetic algorithms. Results of the application of our method are presented.
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Zagal, J.C., Ruiz-del-Solar, J., Guerrero, P., Palma, R. (2004). Evolving Visual Object Recognition for Legged Robots. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds) RoboCup 2003: Robot Soccer World Cup VII. RoboCup 2003. Lecture Notes in Computer Science(), vol 3020. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25940-4_16
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DOI: https://doi.org/10.1007/978-3-540-25940-4_16
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