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

共 50 条

[21] EYE-IN-HAND ROBOTIC GRIPPER VISION FUSION FOR OBJECT RECOGNITION AND TRACKING
Liu, Shih-Wei
Chang, Jen-Yuan
PROCEEDINGS OF THE ASME 28TH CONFERENCE ON INFORMATION STORAGE AND PROCESSING SYSTEMS, 2019, 2019,
[22] Hybrid neural network vision system for object identification
Lee, C.-M.
Patterson, D.
Proceedings of the International Conference on Artificial Neural Networks, 1991,
[23] Image Feature Extraction and Object Recognition Based on Vision Neural Mechanism
Wei, Peng Cheng
Zou, Yang
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2020, 34 (06)
[24] Object recognition using local invariant features for robotic applications: A survey
Loncomilla, Patricio
Ruiz-del-Solar, Javier
Martinez, Luz
PATTERN RECOGNITION, 2016, 60 : 499 - 514
[25] Recognition of quadratic surface of revolution using a robotic vision system
Tsai, MJ
Hwung, JH
Lu, TF
Hsu, HY
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2006, 22 (02) : 134 - 143
[26] Efficient deep network for vision-based object detection in robotic applications
Lu, Keyu
An, Xiangjing
Li, Jian
He, Hangen
NEUROCOMPUTING, 2017, 245 : 31 - 45
[27] Vision-based Robotic Arm in Defect Detection and Object Classification Applications
Lin, Cheng-Jian
Jhang, Jyun-Yu
Gao, Yi-Jyun
Huang, Hsiu-Mei
SENSORS AND MATERIALS, 2024, 36 (02) : 655 - 670
[28] Development of a vision based object classification system for an industrial robotic manipulator
Köker, R
Öz, C
Ferikoglu, A
ICECS 2001: 8TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS AND SYSTEMS, VOLS I-III, CONFERENCE PROCEEDINGS, 2001, : 1281 - 1284
[29] Context-based vision system for place and object recognition
Torralba, A
Murphy, KP
Freeman, WT
Rubin, MA
NINTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS I AND II, PROCEEDINGS, 2003, : 273 - 280
[30] Object Recognition Using Multidirectional Vision System on Soccer Robot
Kusumawardhana, Dhimas Bintang
Mutijarsa, Kusprasapta
2017 4TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY SYSTEMS AND INNOVATION (ICITSI), 2017, : 183 - 187

← 1 2 3 4 5 →