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Influence of vision systems, black and white, colored and visual digitalization, in natural cork stopper quality estimation
Author(s) -
Costa Augusta,
Pereira Helena
Publication year - 2007
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.2947
Subject(s) - cork , artificial intelligence , rgb color model , colored , computer vision , visual inspection , machine vision , pattern recognition (psychology) , computer science , linear discriminant analysis , image processing , image (mathematics) , materials science , composite material
Quality classification of wine natural cork stoppers is related to presence of discontinuities in the cork tissue. Automated image analysis of stoppers based on black and white cameras is used industrially for commercial classification but recently color has been introduced in image processing. This paper compares the performance of three image vision systems regarding classification accuracy of cork stoppers of good, medium and inferior quality: black and white, three‐band RGB color and manual detection by digitalization in color image. A canonical discriminant analysis approach was used to compare the discriminating power between cork stopper quality in each vision system. Good discriminant results were obtained with the area of pores expressed either in total or as ratio, mean or maximum value. The use of color slightly enlarges the range of cork inspection systems and automated systems have a similar accuracy of classification to visual inspection. Copyright © 2007 Society of Chemical Industry