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Manifold learning in machine vision and robotics
Author(s): Alexander Bernstein
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Paper Abstract

Smart algorithms are used in Machine vision and Robotics to organize or extract high-level information from the available data. Nowadays, Machine learning is an essential and ubiquitous tool to automate extraction patterns or regularities from data (images in Machine vision; camera, laser, and sonar sensors data in Robotics) in order to solve various subject-oriented tasks such as understanding and classification of images content, navigation of mobile autonomous robot in uncertain environments, robot manipulation in medical robotics and computer-assisted surgery, and other. Usually such data have high dimensionality, however, due to various dependencies between their components and constraints caused by physical reasons, all „feasible and usable data‟ occupy only a very small part in high dimensional „observation space‟ with smaller intrinsic dimensionality. Generally accepted model of such data is manifold model in accordance with which the data lie on or near an unknown manifold (surface) of lower dimensionality embedded in an ambient high dimensional observation space; real-world high-dimensional data obtained from „natural‟ sources meet, as a rule, this model. The use of Manifold learning technique in Machine vision and Robotics, which discovers a low-dimensional structure of high dimensional data and results in effective algorithms for solving of a large number of various subject-oriented tasks, is the content of the conference plenary speech some topics of which are in the paper.

Paper Details

Date Published: 8 February 2017
PDF: 6 pages
Proc. SPIE 10253, 2016 International Conference on Robotics and Machine Vision, 102530G (8 February 2017); doi: 10.1117/12.2270550
Show Author Affiliations
Alexander Bernstein, Skolkovo Institute of Science and Technology (Russian Federation)
Institute for Systems Analysis (Russian Federation)


Published in SPIE Proceedings Vol. 10253:
2016 International Conference on Robotics and Machine Vision
Alexander V. Bernstein; Adrian Olaru; Jianhong Zhou, Editor(s)

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