Browsing by Author "Mendoza, F"

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    Application of image analysis for classification of ripening bananas
    (WILEY, 2004) Mendoza, F; Aguilera, JM
    A computer vision system was implemented to identify the ripening stages of bananas based on color, development of brown spots, and image texture information. Nine simple features of appearance (L* a*, b* values; brown area percentage, number of brown spots per cm(2); and homogeneity, contrast, correlation: and entropy of image texture) extracted from images of bananas were used for classification purposes. Results show that in spite of variations in data for color and appearance, a simple classification technique is as good to identify the ripening stages of bananas as professional visual perception. Using L* a*, b* bands, brown area percentage, and contrast,it was possible to classify 49 banana samples in their 7 ripening stages with an accuracy of 98%. Computer vision shows promise for online prediction of ripening stages of bananas.
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    Classification of potato chips using pattern recognition
    (WILEY, 2004) Pedreschi, F; Mery, D; Mendoza, F; Aguilera, JM
    An approach to classify potato chips using pattern recognition from color digital images consists of 5 steps: (1) image acquisition, (2) preprocessing, (3) segmentation, (4) feature extraction, and (5) classification. Ten chips prepared for each of the following 6 conditions were examined: 2 pretreatments (blanched and unblanched) at 3 temperatures (120 degreesC, 150 degreesC, and 180 degreesC). More than 1500 features were extracted from each of the 60 images. Finally, 11 features were selected according to their classification attributes. Seven different classification cases (for example, classification of the 6 classes or distinction between blanched and unblanched samples) were analyzed using the selected features. Although samples were highly heterogeneous, using a simple classifier and a small number of features, it was possible to obtain a good performance value in all cases: classification of the 6 classes was in the confidence interval between 78% and 89% with a probability of 95%.