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

Jan Figat , Włodzimierz Kasprzak


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
Author Jan Figat IAiIS
Jan Figat,,
- The Institute of Control and Computation Engineering
, Włodzimierz Kasprzak IAiIS
Włodzimierz Kasprzak,,
- The Institute of Control and Computation Engineering
Publication size in sheets0.5
Book Szewczyk Roman, Kaliczyńska Małgorzata, Zieliński Cezary: Challenges in Automation, Robotics and Measurement Techniques. Proceedings of AUTOMATION-2016, March 2-4, 2016, Warsaw, Poland, Advances in Intelligent Systems and Computing, vol. 440, 2016, Springer International Publishing, ISBN 978-3-319-29356-1, [978-3-319-29357-8], 919 p., DOI:10.1007/978-3-319-29357-8
Keywords in EnglishInductive learning Version spaces 3D objects Model acquisition Robot perception
projectRobREx: Autonomy for rescue and exploration robots. Project leader: Zieliński Cezary, , Phone: 5102, start date 12-12-2012, end date 30-11-2015, 513/1031, Completed
WEiTI Projects financed by NCRD [Projekty finansowane przez NCBiR (NCBR)]
Languageen angielski
Figat Kasprzak automation_2016.pdf 808.77 KB
Score (nominal)15
ScoreMinisterial score = 15.0, 27-03-2017, BookChapterSeriesAndMatConf
Ministerial score (2013-2016) = 15.0, 27-03-2017, BookChapterSeriesAndMatConf
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