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Proceedings Paper

Bubble structure evaluation method of sponge cake by suing image morphology
Author(s): Kunihito Kato; Kazuhiko Yamamoto; Masahiko Nonaka; Yukiyo Katsuta; Chinatsu Kasamatsu
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Paper Abstract

Nowadays, many evaluation methods for food industry by using image processing are proposed. These methods are becoming new evaluation method besides the sensory test and the solid-state measurement that have been used for the quality evaluation recently. The goal of our research is structure evaluation of sponge cake by using the image processing. In this paper, we propose a feature extraction method of the bobble structure in the sponge cake. Analysis of the bubble structure is one of the important properties to understand characteristics of the cake from the image. In order to take the cake image, first we cut cakes and measured that's surface by using the CIS scanner, because the depth of field of this type scanner is very shallow. Therefore the bubble region of the surface has low gray scale value, and it has a feature that is blur. We extracted bubble regions from the surface images based on these features. The input image is binarized, and the feature of bubble is extracted by the morphology analysis. In order to evaluate the result of feature extraction, we compared correlation with "Size of the bubble" of the sensory test result. From a result, the bubble extraction by using morphology analysis gives good correlation. It is shown that our method is as well as the subjectivity evaluation.

Paper Details

Date Published: 29 May 2007
PDF: 8 pages
Proc. SPIE 6356, Eighth International Conference on Quality Control by Artificial Vision, 635612 (29 May 2007); doi: 10.1117/12.736930
Show Author Affiliations
Kunihito Kato, Gifu Univ. (Japan)
Kazuhiko Yamamoto, Gifu Univ. (Japan)
Masahiko Nonaka, Ajinomoto Co., Inc. (Japan)
Yukiyo Katsuta, Ajinomoto Co., Inc. (Japan)
Chinatsu Kasamatsu, Ajinomoto Co., Inc. (Japan)

Published in SPIE Proceedings Vol. 6356:
Eighth International Conference on Quality Control by Artificial Vision
David Fofi; Fabrice Meriaudeau, Editor(s)

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