A calibration method for binocular vision system based on CPSO-BP Netural Network

被引：1

作者：

Zhou, Jun ^{[1
]}

Xia, Tao ^{[1
]}

Wang, Ting-ting ^{[1
]}

Liao, Hua-li ^{[1
]}

Fu, Yu-ping ^{[1
]}

机构：

[1] Hohai Univ, Coll Mech & Elect Engn, Changzhou 213022, Peoples R China

来源：

MECHANICAL DESIGN AND POWER ENGINEERING, PTS 1 AND 2 | 2014年 / 490-491卷

关键词：

CPSO; neural network; camera calibration; binocular vision; CAMERA CALIBRATION;

D O I：

10.4028/www.scientific.net/AMM.490-491.1686

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

This paper presents a new calibration method for binocular vision system, based on CPSO-BP neural network. Firstly, the training set of the back propagation (BP) neural network is formed by the image feature point extracted from the binocular vision system. Then the cooperate particle swarm optimization (CPSO) algorithm is introduced to optimize the weights of the BP neural network, making the network with a stronger ability of the global optimization. Experimental results demonstrate that the proposed CPSO-BP-based algorithm has a higher calibration precision than the traditional BP-based calibration method.

引用

页码：1686 / +

页数：2

共 8 条

[1]

Andries P., 2007, ENGELBRECHT COMPUTAT

[2]

Chen Xiaobo, 2010, INT C COMP APPL SYST

[3]

GE DY, 2008, INT C COMP SCI INF T, P632

[4]

Piao H., 2009, Mediterranean Journal of Measurement and Control, V5, P60

[5]

TSAI RY, 1987, IEEE T ROBOTIC AUTOM, V3, P323, DOI 10.1109/JRA.1987.1087109

[6] Improved Binocular Vergence Control via a Neural Network That Maximizes an Internally Defined Reward [J].

Wang, Yiwen ;

Shi, Bertram E. .

IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT, 2011, 3 (03) :247-256

[7] An Efficient Method for Camera Calibration Using MultiLayer Perceptron Type Neural Network [J].

Woo, Dong-Min ;

Park, Dong-Chul .

INTERNATIONAL CONFERENCE ON FUTURE COMPUTER AND COMMUNICATIONS, PROCEEDINGS, 2009, :358-362

[8] A flexible new technique for camera calibration [J].

Zhang, ZY .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2000, 22 (11) :1330-1334

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