Specifications
proposed methods can provide accurate estimation of IM, and achieve similar performance
compared with traditional calibration-based method. Therefore, the proposed methods can be
applied to any robot control system in variational environments, and can realize instant operation
to planar object with complex and unknown shape at large displacement. © 2013 South China
University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of
Sciences and Springer-Verlag Berlin Heidelberg.
Number of references: 14
Main heading: Visual servoing
Controlled terms: Approximation algorithms - Calibration - Kalman filters -
Robot applications
Uncontrolled terms: Accurate estimation - Decoupled control - Digital image -
Estimation algorithm - Extended interaction - Image moments - Image-based -
Interaction matrices - Large displacements - Point-based - Positioning tasks -
Robot control systems - Un-calibrated camera
Classification code: 944 Moisture, Pressure and Temperature, and Radiation Measuring
Instruments - 943 Mechanical and Miscellaneous Measuring Instruments - 942 Electric
and Electronic Measuring Instruments - 941 Acoustical and Optical Measuring Instruments -
921 Mathematics - 732 Control Devices - 731 Automatic Control Principles and
Applications
DOI: 10.1007/s11768-013-0271-7
Database: Compendex
Compilation and indexing terms, © 2013 Elsevier Inc.
11.
Accession number: 20130415923799
Title: Hybrid Monte Carlo sampling implementation of Bayesian support vector machine
Authors: Zhou, Yatong1 ; Li, Jin1 ; Liu, Long2/;;刘泷
Author affiliation:
1 School of Information Engineering, Hebei University of Technology, China
2 School of automation, Xi'an University of Technology, China
Corresponding author: Zhou, Y. (zyt@hebut.edu.cn)
Source title: Advances in Information Sciences and Service Sciences
Abbreviated source title: Adv. Inf. Sci. Serv. Sci.
Volume: 5
Issue: 1
Issue date: 2013
Publication year: 2013
Pages: 284-290
Language: English
ISSN: 19763700
E-ISSN: 22339345
Document type: Journal article (JA)
Publisher: Advanced Institute of Convergence Information Technology, Myoungbo Bldg
3F,, Bumin-dong 1-ga, Seo-gu, Busan, 602-816, Korea, Republic of
Abstract: The Bayesian support vector machine (BSVM) is a probabilistic machine learning