Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002
Bevilacqua, M., Bucci, R., Magrì, A. D., Magrì, A. L., Nescatelli, R., & Marini, F. (2013). Classification and Class-Modelling. Chemometrics in Food Chemistry, 171-233. doi:10.1016/b978-0-444-59528-7.00005-3
Manning, C. D., Raghavan, P., & Schutze, H. (2008). Introduction to Information Retrieval. doi:10.1017/cbo9780511809071
[+]
Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002
Bevilacqua, M., Bucci, R., Magrì, A. D., Magrì, A. L., Nescatelli, R., & Marini, F. (2013). Classification and Class-Modelling. Chemometrics in Food Chemistry, 171-233. doi:10.1016/b978-0-444-59528-7.00005-3
Manning, C. D., Raghavan, P., & Schutze, H. (2008). Introduction to Information Retrieval. doi:10.1017/cbo9780511809071
MacQueen J Some methods for classification and analysis of multivariate observations Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability Berkeley, CA University of California Press 1967 281 297
Haralick, R. M. (1979). Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5), 786-804. doi:10.1109/proc.1979.11328
Felzenszwalb, P. F., & Huttenlocher, D. P. (2004). Efficient Graph-Based Image Segmentation. International Journal of Computer Vision, 59(2), 167-181. doi:10.1023/b:visi.0000022288.19776.77
Barker, M., & Rayens, W. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17(3), 166-173. doi:10.1002/cem.785
Postma, G. J., Krooshof, P. W. T., & Buydens, L. M. C. (2011). Opening the kernel of kernel partial least squares and support vector machines. Analytica Chimica Acta, 705(1-2), 123-134. doi:10.1016/j.aca.2011.04.025
Vitale, R., de Noord, O. E., & Ferrer, A. (2014). A kernel-based approach for fault diagnosis in batch processes. Journal of Chemometrics, 28(8), S697-S707. doi:10.1002/cem.2629
Prats-Montalbán, J. M., & Ferrer, A. (2007). Integration of colour and textural information in multivariate image analysis: defect detection and classification issues. Journal of Chemometrics, 21(1-2), 10-23. doi:10.1002/cem.1026
Prats-Montalbán J Control estadístico de procesos mediante análisis multivariante de imágenes Ph.D. Thesis 2005
López, F., Prats, J. M., Ferrer, A., & Valiente, J. M. (2006). Defect Detection in Random Colour Textures Using the MIA T2 Defect Maps. Image Analysis and Recognition, 752-763. doi:10.1007/11867661_68
Ho, P.-G. (Ed.). (2011). Image Segmentation. doi:10.5772/628
Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition, 26(9), 1277-1294. doi:10.1016/0031-3203(93)90135-j
MATLAB R2012b (8.0.0.783), Natick, USA: The Mathworks Inc
Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1-3), 37-52. doi:10.1016/0169-7439(87)80084-9
Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1-17. doi:10.1016/0003-2670(86)80028-9
Cao, D.-S., Liang, Y.-Z., Xu, Q.-S., Hu, Q.-N., Zhang, L.-X., & Fu, G.-H. (2011). Exploring nonlinear relationships in chemical data using kernel-based methods. Chemometrics and Intelligent Laboratory Systems, 107(1), 106-115. doi:10.1016/j.chemolab.2011.02.004
Vitale, R., de Noord, O. E., & Ferrer, A. (2015). Pseudo-sample based contribution plots: innovative tools for fault diagnosis in kernel-based batch process monitoring. Chemometrics and Intelligent Laboratory Systems, 149, 40-52. doi:10.1016/j.chemolab.2015.09.013
Hirschfeld, H. O. (1935). A Connection between Correlation and Contingency. Mathematical Proceedings of the Cambridge Philosophical Society, 31(4), 520-524. doi:10.1017/s0305004100013517
[-]