Authors:
Beatriz Paniagua-Paniagua
;
Miguel A. Vega-Rodríguez
;
Juan A. Gómez-Pulido
and
Juan M. Sánchez-Pérez
Affiliation:
Univ. Extremadura, Escuela Politécnica, Spain
Keyword(s):
Image processing, industrial application, cork quality, automated visual inspection system, classifiers.
Related
Ontology
Subjects/Areas/Topics:
Image Processing
;
Informatics in Control, Automation and Robotics
;
Robotics and Automation
Abstract:
In this paper we study the use of different classifiers to solve a classification problem existing in the cork industry: the cork stopper/disk classification according to their quality using a visual inspection system. Cork is a natural and heterogeneous material, therefore, its automatic classification (usually, seven different quality classes exist) is very difficult. The classifiers, which we present in this paper, work with several quality discriminators (features), that we think could influence cork quality. These discriminators (features) have been checked and evaluated before being used by the different classifiers that will be exposed here. In this paper we attempt to evaluate the performance of a total of 4 different cork quality-based classifiers in order to conclude which of them is the most appropriate for this industry, and therefore, obtains the best cork classification results. In conclusion, our experiments show that the Euclidean classifier is the one which obtains t
he best results in this application field.
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