A flexible visual inspection system based on neural networks

Authors:
P. Liatsis;J. Y. Goulermas;X. -J. Zeng;E. Milonidis
Affiliations:
School of Engineering and Mathematical Sciences, City University, London, UK;Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK;School of Informatics, University of Manchester, Manchester, UK;School of Engineering and Mathematical Sciences, City University, London, UK
Venue:
International Journal of Systems Science - Innovative Production Machines and Systems, Guest Editors: Duc-Truong Pham, Anthony Soroka and Eldaw Eldukhri
Year:
2009

Citing 12
Cited 0

Machine vision: the eyes of automation

Machine vision: the eyes of automation
The computational brain

The computational brain
Object and Texture Classification Using Higher Order Statistics

IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Self-Organizing Methods in Modeling: Gmdh Type Algorithms

Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Modified high-order neural network for invariant pattern recognition

Pattern Recognition Letters
Pattern Recognition, Third Edition

Pattern Recognition, Third Edition
Image Processing, Analysis, and Machine Vision

Image Processing, Analysis, and Machine Vision
Invariant and adaptive geometrical texture features for defect detection and classification

Pattern Recognition
NN automated defect detection based on optimized thresholding

ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
A Bayesian framework for multilead SMD post-placement quality inspection

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning polynomial feedforward neural networks by genetic programming and backpropagation

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, we propose a neural networks-based machine vision system, which is intended to act as a reconfigurable inspection tool, for use in manufacturing environments. The processing engine of the system is a second-order neural network, which extracts geometric features invariant to translation and rotation. A major issue with the use of higher-order neural networks is the combinatorial explosion of the higher-order terms, which is addressed here with the use of the alternative image representation strategy of coarse coding. We developed a genetic algorithms tool, which allows the automated determination of the optimal number of hidden units in the neural networks architecture. The inspection system is tested in two application areas, namely inspection of axisymmetric components and classification of rivets. Numerous tests are carried out to evaluate the robustness of the proposed system to complex noise sources.