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Proceedings Paper

Neural network classification technique and machine vision for bread crumb grain evaluation
Author(s): Inna Y. Zayas; O. K. Chung; M. Caley
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Paper Abstract

Bread crumb grain was studied to develop a model for pattern recognition of bread baked at Hard Winter Wheat Quality Laboratory (HWWQL), Grain Marketing and Production Research Center (GMPRC). Images of bread slices were acquired with a scanner in a 512 multiplied by 512 format. Subimages in the central part of the slices were evaluated by several features such as mean, determinant, eigen values, shape of a slice and other crumb features. Derived features were used to describe slices and loaves. Neural network programs of MATLAB package were used for data analysis. Learning vector quantization method and multivariate discriminant analysis were applied to bread slices from what of different sources. A training and test sets of different bread crumb texture classes were obtained. The ranking of subimages was well correlated with visual judgement. The performance of different models on slice recognition rate was studied to choose the best model. The recognition of classes created according to human judgement with image features was low. Recognition of arbitrarily created classes, according to porosity patterns, with several feature patterns was approximately 90%. Correlation coefficient was approximately 0.7 between slice shape features and loaf volume.

Paper Details

Date Published: 3 October 1995
PDF: 17 pages
Proc. SPIE 2597, Machine Vision Applications, Architectures, and Systems Integration IV, (3 October 1995); doi: 10.1117/12.223994
Show Author Affiliations
Inna Y. Zayas, USDA Agricultural Research Service (United States)
O. K. Chung, USDA Agricultural Research Service (United States)
M. Caley, USDA Agricultural Research Service (United States)

Published in SPIE Proceedings Vol. 2597:
Machine Vision Applications, Architectures, and Systems Integration IV
Bruce G. Batchelor; Susan Snell Solomon; Frederick M. Waltz, Editor(s)

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