Improving the industrial classification of cork stoppers by using image processing and Neuro-Fuzzy computing

Authors:
Beatriz Paniagua;Miguel A. Vega-Rodríguez;Juan A. Gomez-Pulido;Juan M. Sanchez-Perez
Affiliations:
Department of Technologies of Computers and Communications, University of Extremadura, Cáceres, Spain 10071;Department of Technologies of Computers and Communications, University of Extremadura, Cáceres, Spain 10071;Department of Technologies of Computers and Communications, University of Extremadura, Cáceres, Spain 10071;Department of Technologies of Computers and Communications, University of Extremadura, Cáceres, Spain 10071
Venue:
Journal of Intelligent Manufacturing
Year:
2010

Citing 9
Cited 0

Texture Measures for Carpet Wear Assessment

IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of thresholding techniques

Computer Vision, Graphics, and Image Processing
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Computer Vision

Computer Vision
A wavelet-based neuro-fuzzy system for data mining small image sets

ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
A Statistical Approach to Texture Classification from Single Images

International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Prediction of flow stress for carbon steels using recurrent self-organizing neuro fuzzy networks

Expert Systems with Applications: An International Journal
A Neuro-fuzzy Approach to User Attention Recognition

ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Cork quality classification system using a unified image processing and fuzzy-neural network methodology

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents a solution to a problem existing in the cork industry: cork stopper/disk classification according to their quality using a visual inspection system. Cork is a natural and heterogeneous (remarkable variability among different samples, being impossible to find two samples with the same morphological distribution in its defects) material; therefore, its automatic classification (seven quality classes exist) is very difficult. The solution proposed in this paper evaluates the following procedures: quality discriminatory features extraction and classifiers analysis. Each procedure focused on the study of aspects that could influence cork quality. Experiments show that the best results are obtained by system specific features: cork area occupied by defects (after thresholding), size of the biggest defect within the cork area (morphological operations), and the Laws TEMs E5L5TR, E5E5TR, S5S5TR, W5W5TR, all working on a Neuro-Fuzzy classifier. In conclusion, the results of this study represent an important contribution to improve quality control in the cork industry.