Incremental Version Space Merging Approach to 3D Object Model Acquisition for Robot Vision

  • Jan Figat
  • Włodzimierz Kasprzak
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 440)


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.


Inductive learning Version spaces 3D objects Model acquisition Robot perception 



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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Control and Computation EngineeringWarsaw University of TechnologyWarsawPoland

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