Abstract
In order to automize on-line selection of biological products, it is necessary to determine relationships between human sensory evaluation (like the beauty of flowerplants), and physical measurements on objects (like machine vision images). Classical methods of image processing and statistics, are combined with neural network techniques. The research deals with methods for the selection of significant parameters for the judgement, and methods for decision learning and generation: for both types of methods, classical statistics and neural network technics are either compared or combined. Interest of the various combinations are discussed, through the application on beauty selection of flowerplants.
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References
Journot V. 1991. Détermination d'un indice de la qualité des produits agricoles en frais. Thesis at the Institut National Agronomique, Paris-Grignon (in French).
Malek D.M., Munroe J.H., Schmitt D.J. and Korth B.. 1986. Statistical evaluation of sensory judges. Am. Soc. Brew. Chem. J.. 49(1):23.
Martens M. 1985. Sensory and chemical quality criteria for white cabbage studied by multivariate data analysis. Lebensmittel Wissenschaft und Techn. 18:100–104.
McDaniel M., Henderson L.A., Watson B.T.Jr. and Heatherball D.. 1987. Sensory panel training and screening for descriptive analysis of Pinot Noir wine fermented by several strains of malolactic bacteria. Journal Sensory Studies. 2:149.
Lippmann R.L.. 1987. An introduction to computing with neural nets. IEEE ASSP Magazine. Vol. 4. P4–22.
Pao Y. 1989. Adaptive Pattern Recognition and Neural Networks. Addison Wesley Pub. Comp.
Papelier S. 1990. Applications de la vision assistée par ordinateur à l'étude de la qualité du cyclamen. Report CNIH (in French).
Powers J.J. 1984. Using general statistical programs to evaluate sensory data. Food Technology 38(6):74–84.
Powers J.J. 1988. Uses of multivariate methods in screeening and training sensory panelists. Food Technology 42(11):123–127.
Resurreccion A.V.A. 1988. Applications of multivariate methods in food quality evaluation. 42(11):128–136.
Rumelhart, D.E., J.L.McClelland. 1986. Parallel Distributed Processing. MIT Press.
Slaughter, D.C., R.C. Harrell 1989. Discriminating fruit for robotic harvest using colour in natural outdoor scenes. TRANS. of the ASAE March–April 32(2):757–763.
Tillett R.D.. 1991. Image Analysis for Agricultural Processes: A Review of Potential Opportunities. J. Agricultural Engineering Research. Vol. 50(4): 247–258.
Tomassonne R. 1983. La régression linéaire: Nouveaux regards sur une ancienne méthode statistique. MASSON (in French).
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© 1993 Springer-Verlag Berlin Heidelberg
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Ros, F., Brons, A., Sevila, F., Rabatel, G., Touzet, C. (1993). Combination of neural network and statistical methods for sensory evalution of biological products: On-line beauty selection of flowers. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_228
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DOI: https://doi.org/10.1007/3-540-56798-4_228
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