UNSUPERVISED LEARNING FOR RIPENESS ESTIMATION FROM GRAPE SEEDS IMAGES

[1]Adams, D.O. Phenolics and Ripening in Grape Berries. American Journal of Enology and Viticulture 2006, 223 57, 249–256.10.5344/ajev.2006.57.3.249 Search in Google Scholar

[2]Rodriguez-Pulido, F.J.; Ferrer-Gallego, R.; Gonzalez-Miret, M.L.; Rivas-Gonzalo, J.C.; Escribano-Bailon, M.T.; Heredia, F.J. Preliminary study to determine the phenolic maturity stage of grape seeds by computer vision. Analytica Chimica Acta 2012, 732, 78 – 82. A selection of papers presented at In Vino Analytica Scientia.10.1016/j.aca.2012.01.00522688037 Search in Google Scholar

[3]Fredes, C.; Bennewitz, E.V.; Holzapfel, E.; Saavedra, F. Relation between Seed Appearance and Phenolic Maturity: A Case Study Using Grapes cv. Carmenere. Chilean Journal of Agricultural Research 2010, 70, 381–389. Search in Google Scholar

[4]Rodriguez-Pulido, F.J.; Gomez-Robledo, L.; Melgosa, M.; Gordillo, B.; Gonzalez-Miret, M.L.; Heredia, F.J. Ripeness estimation of grape berries and seeds by image analysis. Computers and Electronics in Agriculture 2012, 82, 128 – 133. Search in Google Scholar

[5]Avila, F.; Mora, M.; Fredes, C. A method to estimate Grape Phenolic Maturity based on seed images. Computers and Electronics in Agriculture 2014, 101, 76 – 83. Search in Google Scholar

[6]Avila, F.; Mora, M.; Oyarce, M.; Zuniga, A.; Fredes, C. A method to construct fruit maturity color scales based on support machines for regression: Application to olives and grape seeds. Journal of Food Engineering 2015, 162, 9 – 17. Search in Google Scholar

[7]Swain, M.J.; Ballard, D.H. Color Indexing. Int. J. Comput. Vision 1991, 7, 11–32.10.1007/BF00130487 Search in Google Scholar

[8]Lee, D.J.; Archibald, J.K.; Chang, Y.C.; Greco, C.R. Robust color space conversion and color distribution analysis techniques for date maturity evaluation. Journal of Food Engineering 2008, 88, 364–372. Search in Google Scholar

[9]Zhang, D.; Lee, D.J.; Tippetts, B.J.; Lillywhite, K.D. Date maturity and quality evaluation using color distribution analysis and back projection. Journal of Food Engineering 2014, 131, 161–169. Search in Google Scholar

[10]Sefidpour, A.; Bouguila, N. Spatial color image segmentation based on finite non-Gaussian mixture models. Expert Systems with Applications 2012, 39, 8993 – 9001.10.1016/j.eswa.2012.02.024 Search in Google Scholar

[11]Rigouste, L.; Cappé, O.; Yvon, F. Inference and evaluation of the multinomial mixture model for text clustering. Information Processing & Management 2007, 43, 1260 – 1280. Search in Google Scholar

[12]Figueiredo, M.A.T.; Jain, A.K. Unsupervised Learning of Finite Mixture Models. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24, 381–396.10.1109/34.990138 Search in Google Scholar

[13]Bouguila, N.; Ziou, D. Using unsupervised learning of a finite Dirichlet mixture model to improve pattern recognition applications. Pattern Recognition Letters 2005, 26, 1916 – 1925.10.1016/j.patrec.2005.03.016 Search in Google Scholar

[14]Bouguila, N.; Ghimire, M.N. Discrete visual features modeling via leave-one-out likelihood estimation and applications. Journal of Visual Communication and Image Representation 2010, 21, 613 – 626.10.1016/j.jvcir.2010.04.001 Search in Google Scholar

[15]Ng, K.W.; Tian, G.L.; Tang, M.L. Dirichlet and related distributions: Theory, methods and applications; Vol. 888, John Wiley & Sons, 2011.10.1002/9781119995784 Search in Google Scholar

[16]Minka, T.P. Estimating a Dirichlet distribution. Technical report, 2003. Search in Google Scholar

[17]Otsu, N. A Threshold Selection Method from Gray-Level Histograms. Systems, Man and Cybernetics, IEEE Transactions on 1979, 9, 62–66.10.1109/TSMC.1979.4310076 Search in Google Scholar

[18]Belongie, S.; Carson, C.; Greenspan, H.; Malik, J. Color- and texture-based image segmentation using EM and its application to content-based image retrieval. Computer Vision, 1998. Sixth International Conference on, 1998, pp. 675–682. Search in Google Scholar

[19]Xiao, H.W.; Zhang, J. Image Segmentation algorithm based on color features: Case study with giant panda, International Journal on Smart Sensing and Intelligent Systems 2016, 9, 799-817. Search in Google Scholar

[20]Murphy, K.P. Machine Learning: A Probabilistic Perspective; The MIT Press, 2012. Search in Google Scholar