Abstract
If a robot shall learn from visual data the task is greatly simplified if visual data is abstracted from pixel data into basic shapes or Gestalts. This paper introduces a method of processing images to abstract basic features into higher level Gestalts. Grouping is formulated as incremental problem to avoid grouping parameters and to obtain anytime processing characteristics. The proposed system allows shape detection of 3D such as cubes, cones and cylinders for robot affordance learning.
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Richtsfeld, A., Vincze, M. (2009). 3D Shape Detection for Mobile Robot Learning. In: Kröger, T., Wahl, F.M. (eds) Advances in Robotics Research. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01213-6_10
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DOI: https://doi.org/10.1007/978-3-642-01213-6_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01212-9
Online ISBN: 978-3-642-01213-6
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