A neural multiclassifier system for object recognition in robotic vision applications

被引：14

作者：

Mitzias, DA ^{[1
]}

Mertzios, BG ^{[1
]}

机构：

[1] Democritus Univ Thrace, Dept Elect & Comp Engn, Automat Control Syst Lab, GR-67100 Xanthi, Hellas, Greece

来源：

MEASUREMENT | 2004年 / 36卷 / 3-4期

关键词：

robot vision; neural networks; multiple classifiers; feature extraction methods; object recognition; statistical moments; polygon approximation;

D O I：

10.1016/j.measurement.2004.09.008

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

A robotic vision system has been developed that is based on the optical recognition of objects to be manipulated, that are located in the workspace of the robotic manipulator. The developed system has a low development and operation cost, is controlled via an external computer and operates in an unstructured complex environment. The vision system is desired to recognize the objects, which are placed in the workspace and also to identify the exact position and orientation of each particular object, in order to lead the robot manipulator system. For the recognition of objects, a high performance NEural MUlticlassifier System (NEMUS) is presented, which combines multiple classifiers that operate on different feature sets. NEWS is characterized by a great degree of modularity and flexibility and is very efficient for demanding and generic pattern recognition applications. (C) 2004 Elsevier Ltd. All rights reserved.

引用

页码：315 / 330

页数：16

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