naa a22 4500 51072664X CHVBK 20180411082938.0 cr unu---uuuuu 180411e20130801xx s 000 0 eng 10.1007/s11947-012-0934-2 doi (NATIONALLICENCE)springer-10.1007/s11947-012-0934-2 Automated Design of a Computer Vision System for Visual Food Quality Evaluation [Elektronische Daten] [Domingo Mery, Franco Pedreschi, Alvaro Soto] Considerable research efforts in computer vision applied to food quality evaluation have been developed in the last years; however, they have been concentrated on using or developing tailored methods based on visual features that are able to solve a specific task. Nevertheless, today's computer capabilities are giving us new ways to solve complex computer vision problems. In particular, a new paradigm on machine learning techniques has emerged posing the task of recognizing visual patterns as a search problem based on training data and a hypothesis space composed by visual features and suitable classifiers. Furthermore, now we are able to extract, process, and test in the same time more image features and classifiers than before. Thus, we propose a general framework that designs a computer vision system automatically, i.e., it finds—without human interaction—the features and the classifiers for a given application avoiding the classical trial and error framework commonly used by human designers. The key idea of the proposed framework is to select—automatically—from a large set of features and a bank of classifiers those features and classifiers that achieve the highest performance. We tested our framework on eight different food quality evaluation problems yielding a classification performance of 95% or more in every case. The proposed framework was implemented as a Matlab Toolbox available for noncommercial purposes. Springer Science+Business Media, LLC, 2012 Food quality evaluation nationallicence Feature extraction nationallicence Feature selection nationallicence Classification nationallicence Pattern recognition nationallicence Image analysis nationallicence Image segmentation nationallicence Image processing nationallicence Computer vision nationallicence Mery Domingo Machine Intelligence Group ASIS-UC Interdisciplinary Research Program on Tasty and Healthy Foods, Department of Computer Science, Pontificia Universidad Catolica de Chile, Av. Vicuña Mackenna 4860, Santiago, Chile aut Pedreschi Franco ASIS-UC Interdisciplinary Research Program on Tasty and Healthy Foods, Department of Chemical Engineering and Bioprocesses, Pontificia Universidad Catolica de Chile, Santiago, Chile aut Soto Alvaro Machine Intelligence Group, Department of Computer Science, Pontificia Universidad Catolica de Chile, Santiago, Chile aut Food and Bioprocess Technology Springer US; http://www.springer-ny.com 6/8(2013-08-01), 2093-2108 1935-5130 6:8<2093 2013 6 11947 https://doi.org/10.1007/s11947-012-0934-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11947-012-0934-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Mery Domingo Machine Intelligence Group ASIS-UC Interdisciplinary Research Program on Tasty and Healthy Foods, Department of Computer Science, Pontificia Universidad Catolica de Chile, Av. Vicuña Mackenna 4860, Santiago, Chile aut NATIONALLICENCE 700 1- Pedreschi Franco ASIS-UC Interdisciplinary Research Program on Tasty and Healthy Foods, Department of Chemical Engineering and Bioprocesses, Pontificia Universidad Catolica de Chile, Santiago, Chile aut NATIONALLICENCE 700 1- Soto Alvaro Machine Intelligence Group, Department of Computer Science, Pontificia Universidad Catolica de Chile, Santiago, Chile aut NATIONALLICENCE 773 0- Food and Bioprocess Technology Springer US; http://www.springer-ny.com 6/8(2013-08-01), 2093-2108 1935-5130 6:8<2093 2013 6 11947 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer