Celebratio Mathematica

T. Wu, G. Polatkan, D. Steel, W. Brown, I. Daubechies, and R. Calderbank: “Painting analysis using wavelets and probabilistic topic models,” pp. 3264–3268 in 2013 IEEE international conference on image processing (Melbourne, 15–18 September 2013). IEEE (Piscataway, NJ), 2013. ArXiv 1401.6638 incollection

In this paper, computer-based techniques for stylistic analysis of paintings are applied to the five panels of the 14th century Peruzzi Altarpiece by Giotto di Bondone. Features are extracted by combining a dual-tree complex wavelet transform with a hidden Markov tree (HMT) model. Hierarchical clustering is used to identify stylistic keywords in image patches, and keyword frequencies are calculated for sub-images that each contains many patches. A generative hierarchical Bayesian model learns stylistic patterns of keywords; these patterns are then used to characterize the styles of the sub-images; this in turn, permits to discriminate between paintings. Results suggest that such unsupervised probabilistic topic models can be useful to distill characteristic elements of style.

A. Robert Calderbank	Related
Tong Wu	Related
Güngör Polatkan	Related
David Steel	Related
William P. Brown	Related

R. Yin, D. Dunson, B. Cornelis, B. Brown, N. Ocon, and I. Daubechies: “Digital cradle removal in X-ray images of art paintings,” pp. 4299–4303 in 2014 IEEE international conference on image processing (ICIP) (Paris, 27–30 October 2014). IEEE (Piscataway, NJ), 2014. incollection

Rujie (Rachel) Yin	Related
Bruno Cornelis	Related
David Brian Dunson	Related
Noelle Ocon	Related
William P. Brown	Related

H. Yang, J. Lu, W. P. Brown, I. Daubechies, and L. Ying: “Quantitative canvas weave analysis using 2-D synchrosqueezed transforms: Application of time-frequency analysis to art investigation,” IEEE Signal Proc. Mag. 32 : 4 (July 2015), pp. 55–63. article

Quantitative canvas weave analysis has many applications in art investigations of paintings, including dating, forensics, and canvas rollmate identification. Traditionally, canvas analysis is based on X-radiographs. Prior to serving as a painting canvas, a piece of fabric is coated with a priming agent; smoothing its surface makes this layer thicker between and thinner right on top of weave threads. These variations affect the X-ray absorption, making the weave pattern stand out in X-ray images of the finished painting. To characterize this pattern, it is customary to visually inspect small areas within the X-radiograph and count the number of horizontal and vertical weave threads; averages of these then estimate the overall canvas weave density. The tedium of this process typically limits its practice to just a few sample regions of the canvas. In addition, it does not capture more subtle information beyond weave density, such as thread angles or variations in the weave pattern. Signal processing techniques applied to art investigation are now increasingly used to develop computer-assisted canvas weave analysis tools.

Jianfeng Lu	Related
Haizhao Yang	Related
Lexing Ying	Related
William P. Brown	Related

year	title	people

			clear

Ingrid Daubechies

Complete Bibliography

Works connected to William P. Brown

Filter the Bibliography List