NDLI: Surface defect detection by a texture analysis with a neural network

Content Provider	IEEE Xplore Digital Library
Author	Branca, A. Delaney, W. Lovergine, F.P. Distante, A.
Copyright Year	1995
Description	Author affiliation: Dept. di Inf., Univ. degli Studi di Bari, Italy (Branca, A.; Delaney, W.)
Abstract	In this paper a neural algorithm for defect detection in industrial inspection is proposed. One of the most difficult problems in process control and automated inspection is the identification, description and classification of surface defects and anomalies. A critical role in surface inspection is played by texture because most of the defects are rich in textural content. The goal of this work is to propose a quantitative and qualitative technique to detect surface defects with oriented texture structure. The neural algorithm, that the authors propose to classify an oriented flow field, can classify each kind of defect with textural characteristics: once the system has been set to recognize a limited number of patterns, it is able to identify and analyze a broader family of patterns. To the surface image to be analyzed is associated a vector field which computes the dominant local orientations of the gradients of the image smoothed with a Gaussian filter. The different defective surface regions are recovered and classified minimizing an energy function by means of a neural network. Experimental results of tests performed on ferromagnetic surfaces show as the proposed neural at algorithm works.
Starting Page	1497
Ending Page	1502
File Size	579376
Page Count	6
File Format	PDF
ISBN	0780319656
ISSN	10504729
DOI	10.1109/ROBOT.1995.525487
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-05-21
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Surface texture Inspection Process control Character recognition Pattern recognition Image texture analysis Pattern analysis Algorithm design and analysis Image analysis Filters
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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