A survey on industrial vision systems, applications and tools

被引：573

作者：

Malamas, EN

Petrakis, EGM ^{[1
]}

Zervakis, M

Petit, L

Legat, JD

机构：

[1] Tech Univ Crete, Dept Elect & Comp Engn, Khania 73100, Crete, Greece

[2] Univ Catholique Louvain, Microelect Lab, B-1348 Louvain, Belgium

来源：

IMAGE AND VISION COMPUTING | 2003年 / 21卷 / 02期

关键词：

machine vision; automated visual inspection; image processing; image analysis;

D O I：

10.1016/S0262-8856(02)00152-X

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The state of the art in machine vision inspection and a critical overview of real-world applications are presented in this paper. Two independent ways to classify applications are proposed, one according to the inspected features of the industrial product or process and the other according to the inspection independent characteristics of the inspected product or process. The most contemporary software and hardware tools for developing industrial vision systems are reviewed. Finally, under the light of recent advances in image sensors, software and hardware technology, important issues and directions for designing and developing industrial vision systems are identified and discussed. (C) 2003 Elsevier Science B.V. All rights reserved.

引用

页码：171 / 188

页数：18

共 95 条

[11]

Braggins D., 1996, Sensor Review, V16, P16, DOI 10.1108/02602289610123512

[12]

Braggins D., 1995, Sensor Review, V15, P11, DOI 10.1108/02602289510085534

[13]

Carpenter Gail A., 1992, IEEE T NEURAL NETWOR, V3

[14] Cork quality classification system using a unified image processing and fuzzy-neural network methodology [J].