Paper Title:
The Robot Target Recognition Based on Support Vector Machine and D-S Evidence Theory
  Abstract

Aiming at the problem of low recognition rate and easy affected by environment during the process of robot target recognition in complex environments, the target recognition method combining support vector machine (SVM) with D-S evidence theory was proposed. Taking citrus recognition as an example, SVM was used by the method to local classification according to citrus color and geometry feature information respectively, and the results of SVM were transformed to probability outputs through Platt model, and treated them as the basic probability assignment (BPA) of D-S evidence theory to reason and fuse local recognition results, and then realized the combination of SVM and D-S evidence theory in citrus recognition, finally improved the recognition rate. The experimental results showed that: the recognition rate of the method combining SVM with D-S evidence theory and integrating color features and geometry features was higher than SVM method with only color or geometry features.

  Info
Periodical
Advanced Materials Research (Volumes 308-310)
Chapter
Intelligent Optimization Design
Edited by
Jian Gao
Pages
1215-1219
DOI
10.4028/www.scientific.net/AMR.308-310.1215
Citation
M. S. Zhu, X. J. Zou, L. J. Chen, H. X. Zou, K. Y. Chen, "The Robot Target Recognition Based on Support Vector Machine and D-S Evidence Theory", Advanced Materials Research, Vols. 308-310, pp. 1215-1219, 2011
Online since
August 2011
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Price
$32.00
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