Manifold learning in machine vision and robotics

Alexander Bernstein

doi:10.1117/12.2270550

8 February 2017 Manifold learning in machine vision and robotics

Alexander Bernstein

Proceedings Volume 10253, 2016 International Conference on Robotics and Machine Vision; 102530G (2017) https://doi.org/10.1117/12.2270550
Event: 2016 International Conference on Robotics and Machine Vision, 2016, Moscow, Russia

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.

Citation Download Citation

Alexander Bernstein "Manifold learning in machine vision and robotics", Proc. SPIE 10253, 2016 International Conference on Robotics and Machine Vision, 102530G (8 February 2017); https://doi.org/10.1117/12.2270550

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available