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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.