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• W2188714923 abstract "Machine vision solutions are becoming a standard for quality inspection in several manufacturing industries. In the processed-food industry where the appearance attributes of the product are essential to customer's satisfaction, visual inspection can be reliably achieved with machine vision. But such systems often involve the extraction of a larger number of features than those actually needed to ensure proper quality control, making the process less efficient and difficult to tune. This work experiments with several feature selection techniques in order to reduce the number of attributes analyzed by a real-time vision-based food inspection system. Identifying and removing as much irrelevant and redundant information as possible reduces the dimensionality of the data and allows classification algorithms to operate faster. In some cases, accuracy on classification can even be improved. Filter-based and wrapper-based feature selectors are experimentally evaluated on different bakery products to identify the best performing approaches. Copyright © 2009 IFSA." @default.
• W2188714923 created "2016-06-24" @default.
• W2188714923 creator A5011570915 @default.
• W2188714923 creator A5064822721 @default.
• W2188714923 date "2009-01-01" @default.
• W2188714923 modified "2023-09-27" @default.
• W2188714923 title "Feature Space Dimensionality Reduction for Real-Time Vision-Based Food Inspection" @default.
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• W2188714923 hasPublicationYear "2009" @default.
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