Abstract
A concept learning algorithm is developed, which uses the visual information generated by a virtual receptor in a robotic system (e.g. symbolic image segments) to create learning examples. Its goal is to detect similarities in the training data and to create an appropriate object model. The version-space, intended to describe the possible concept hypotheses, is generated by a novel IVSM-ID algorithm, the incremental version space merging with imperfect data, that deals with partly imperfect and noisy training data—a common problem in computer vision systems. The generated model takes the form of a graph of constraints with fuzzy predicates. The approach is verified by learning concepts of elementary surface and solid primitives on base of segmented RGB-D images, taken for various light conditions and for different exposure times.
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Acknowledgments
The authors gratefully acknowledge the support of the National Centre for Research and Development (Poland), grant no. PBS1/A3/8/2012.
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Figat, J., Kasprzak, W. (2016). Incremental Version Space Merging Approach to 3D Object Model Acquisition for Robot Vision. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Challenges in Automation, Robotics and Measurement Techniques. ICA 2016. Advances in Intelligent Systems and Computing, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-319-29357-8_49
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DOI: https://doi.org/10.1007/978-3-319-29357-8_49
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