Conference Paper

# Mining the Royal Portrait Miniature for the Art Historical Context

California Univ., Santa Barbara

DOI: 10.1109/ICNSC.2008.4525429 Conference: Networking, Sensing and Control, 2008. ICNSC 2008. IEEE International Conference on Source: IEEE Xplore

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**ABSTRACT:**We present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform. The model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. We develop an efficient algorithm for synthesizing random images subject to these constraints, by iteratively projecting onto the set of images satisfying each constraint, and we use this to test the perceptual validity of the model. In particular, we demonstrate the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set. We also demonstrate the power of our model by successfully synthesizing examples drawn from a diverse collection of artificial and natural textures.International Journal of Computer Vision 02/2001; · 3.62 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**We describe a computational technique for authenticating works of art, specifically paintings and drawings, from high-resolution digital scans of the original works. This approach builds a statistical model of an artist from the scans of a set of authenticated works against which new works then are compared. The statistical model consists of first- and higher-order wavelet statistics. We show preliminary results from our analysis of 13 drawings that at various times have been attributed to Pieter Bruegel the Elder; these results confirm expert authentications. We also apply these techniques to the problem of determining the number of artists that may have contributed to a painting attributed to Pietro Perugino and again achieve an analysis agreeing with expert opinion.Proceedings of the National Academy of Sciences 01/2005; 101(49):17006-10. · 9.81 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**This site provides a clear introduction to the principle component analysis (PCA) without overwhelming the student. The tutorial includes a brief introduction and an explanation of the mathematics of PCA.

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