Quality Classification of Corn Tortillas using Computer Vision. Mery, D., Chanona-Perez, J., Soto, A., Aguilera, J., Cipriano, A., Velez-Riverab, N., Arzate-Vazquez, I., & Gutierrez-Lopez, G. Journal of Food Engineering, 101(4):357-364, 2010. Paper abstract bibtex 1 download Computer vision is playing an increasingly important role in automated visual food inspection. However quality control in tortilla production is still performed by human operators which may lead to misclassification due to their subjectivity and fatigue. In order to reduce the need for human operators and therefore misclassification, we developed a computer vision framework to automatically classify the quality of corn tortillas according to five hedonic sub-classes given by a sensorial panel. The proposed framework analyzed 750 corn tortillas obtained from 15 different Mexican commercial stores which were either small, medium or large in size. More than 2300 geometric and color features were extracted from 1500 images capturing both sides of the 750 tortillas. After implementing a feature selection algorithm, in which the most relevant features were selected for the classification of the five sub-classes, only 64 features were required to design a classifier based on support vector machines. Cross validation yielded a performance of 95% in the classification of the five hedonic sub-classes. Additionally, using only 10 of the selected features and a simple statistical classifier, it was possible to determine the origin of the tortillas with a performance of 96%. We believe that the proposed framework opens up new possibilities in the field of automated visual inspection of tortillas.
@Article{ mery:etal:2010,
author = {D. Mery and J. Chanona-Perez and A. Soto and JM. Aguilera
and A. Cipriano and N. Velez-Riverab and I. Arzate-Vazquez
and G. Gutierrez-Lopez},
title = {Quality Classification of Corn Tortillas using Computer
Vision},
journal = {Journal of Food Engineering},
volume = {101},
number = {4},
pages = {357-364},
year = {2010},
abstract = {Computer vision is playing an increasingly important role
in automated visual food inspection. However quality
control in tortilla production is still performed by human
operators which may lead to misclassification due to their
subjectivity and fatigue. In order to reduce the need for
human operators and therefore misclassification, we
developed a computer vision framework to automatically
classify the quality of corn tortillas according to five
hedonic sub-classes given by a sensorial panel. The
proposed framework analyzed 750 corn tortillas obtained
from 15 different Mexican commercial stores which were
either small, medium or large in size. More than 2300
geometric and color features were extracted from 1500
images capturing both sides of the 750 tortillas. After
implementing a feature selection algorithm, in which the
most relevant features were selected for the classification
of the five sub-classes, only 64 features were required to
design a classifier based on support vector machines. Cross
validation yielded a performance of 95% in the
classification of the five hedonic sub-classes.
Additionally, using only 10 of the selected features and a
simple statistical classifier, it was possible to determine
the origin of the tortillas with a performance of 96%. We
believe that the proposed framework opens up new
possibilities in the field of automated visual inspection
of tortillas. },
url = {http://saturno.ing.puc.cl/media/papers_alvaro/Tortillas-2010.pdf}
}
Downloads: 1
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However quality control in tortilla production is still performed by human operators which may lead to misclassification due to their subjectivity and fatigue. In order to reduce the need for human operators and therefore misclassification, we developed a computer vision framework to automatically classify the quality of corn tortillas according to five hedonic sub-classes given by a sensorial panel. The proposed framework analyzed 750 corn tortillas obtained from 15 different Mexican commercial stores which were either small, medium or large in size. More than 2300 geometric and color features were extracted from 1500 images capturing both sides of the 750 tortillas. After implementing a feature selection algorithm, in which the most relevant features were selected for the classification of the five sub-classes, only 64 features were required to design a classifier based on support vector machines. 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