Quality inspection of textile artificial textures using a neuro-symbolic hybrid system methodology

Authors:
Vianey Guadalupe Cruz Sánchez;Osslan Osiris Vergara Villegas;Gerardo Reyes Salgado;Humberto De Jesús Ochoa Domínguez
Affiliations:
Computer Science Department, Centro Nacional de Investigación y Desarrollo Tecnológico (cenidet), Cuernavaca, Morelos, Mexico;Industrial and Manufacturing Department, Electrical and Computer Department, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico;Computer Science Department, Centro Nacional de Investigación y Desarrollo Tecnológico (cenidet), Cuernavaca, Morelos, Mexico;Electrical and Computer Engineering Department, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico
Venue:
WSEAS Transactions on Computers
Year:
2008

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Abstract

In the industrial sector there are many processes where the visual inspection is essential, the automation of that processes becomes a necessity to guarantee the quality of several objects. In this paper we propose a methodology for textile quality inspection based on the texture cue of an image. To solve this, we use a Neuro-Symbolic Hybrid System (NSHS) that allow us to combine an artificial neural network and the symbolic representation of the expert knowledge. The artificial neural network uses the CasCor learning algorithm and we use production rules to represent the symbolic knowledge. The features used for inspection has the advantage of being tolerant to rotation and scale changes. We compare the results with those obtained from an automatic computer vision task, and we conclude that results obtained using the proposed methodology are better.