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Yang H.,Stanford University | Lu J.,Duke University | Brown W.P.,North Carolina Museum of Art | Daubechies I.,Duke University | Ying L.,Stanford University
IEEE Signal Processing Magazine

Quantitative canvas weave analysis has many applications in art investigations of paintings, including dating, forensics, and canvas rollmate identification [1]?[3]. 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. © 1991-2012 IEEE. Source

Villafana T.E.,Duke University | Brown W.,North Carolina Museum of Art | Warren W.S.,Duke University | Fischer M.,Duke University
Proceedings of SPIE - The International Society for Optical Engineering

We demonstrate that ultrafast pump-probe microscopy provides unique dynamics for natural iron oxide and iron hydroxide earth pigments, despite their chemical similarity. First, we conducted a pump-probe spectroscopy study on heat-treated hematite (the pure red iron oxide mineral) and found the pump-probe dynamics to be temperature dependent. Second, we investigated pottery fired under known conditions and observed firing dependent pump-probe dynamics. Finally, we imaged a New World potshard from the North Carolina Museum of Art. Our results indicate that pump-probe microscopy could be a useful tool in elucidating pottery manufacture. © 2015 SPIE. Source

Wu T.,Rutgers University | Polatkan G.,Twitter | Steel D.,North Carolina Museum of Art | Brown W.,North Carolina Museum of Art | And 2 more authors.
2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

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. © 2013 IEEE. Source

Yin R.,Duke University | Dunson D.,Duke University | Cornelis B.,Free University of Brussels | Brown B.,North Carolina Museum of Art | And 2 more authors.
2014 IEEE International Conference on Image Processing, ICIP 2014

We introduce an algorithm that removes the deleterious effect of cradling on X-ray images of paintings on wooden panels. The algorithm consists of a three stage procedure. Firstly, the cradled regions are located automatically. The second step consists of separating the X-ray image into a textural and image component. In the last step the algorithm learns to distinguish between the texture caused by the wooden cradle and the texture belonging to the original painted wooden panel. The results obtained with our method are compared with those obtained manually by best current practice. © 2014 IEEE. Source

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