Classification of potato chips using pattern recognition
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Date
2004
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Publisher
WILEY
Abstract
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%.
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Keywords
potato chips, color, image analysis, classification, pattern recognition, image texture, feature extraction, COMPUTER VISION, IMAGE-ANALYSIS, COLOR, DEFECTS