Quality Classification of Corn Tortillas using Computer Vision

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.

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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}
}

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