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Vol. 5, Special Issue, March 2009, pp.86-103





Feature Space Dimensionality Reduction for Real-Time Vision-Based Food Inspection


Mai Moussa CHETIMA and Pierre PAYEUR

School of Information Technology and Engineering, University of Ottawa

800 King Edward, Ottawa, ON, Canada, K1N 6N5

Tel.: 613-562-5800

E-mail: m.chetima@uottawa.ca, ppayeur@uottawa.ca



Received: 30 January 2009   /Accepted: 24 February 2009   /Published: 23 March 2009


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 customerís 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.


Keywords: Machine vision, food inspection, quality control, feature selection, machine learning.


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