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Soft Sensor Design Using Image Analysis and its Industrial Applications Part 2. Automatic Quality Classification of Engineered Stone Countertops  

Ryu, Jun-Hyung (Department of Energy & Environmental Systems, Dongguk University)
Liu, J. Jay (Department of Chemical Engineering, Pukyong National University)
Publication Information
Korean Chemical Engineering Research / v.48, no.4, 2010 , pp. 483-489 More about this Journal
Abstract
An image analysis-based soft sensor is designed and applied to automatic quality classification of product appearance with color-textural characteristics. In this work, multiresolutional multivariate image analysis (MR-MIA) is used in order to analyze product images with color as well as texture. Fisher's discriminant analysis (FDA) is also used as a supervised learning method for automatic classification. The use of FDA, one of latent variable methods, enables us not only to classify products appearance into distinct classes, but also to numerically and consistently estimate product appearance with continuous variations and to analyze characteristics of appearance. This approach is successfully applied to automatic quality classification of intermediate and final products in industrial manufacturing of engineered stone countertops.
Keywords
Fisher Discriminant Analysis; Quality Inspection; Visual Appearance; Multiresolutional Multivariate Image Analysis; Color-texture Analysis;
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