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In this work, we describe the application of the ‘steerable pyramid’ (Portilla and Simoncelli, 2000), a high-dimensional texture descriptor, to the discrimination and classification of a large collection of photographic paper textures. This work is part of a broader effort to bring precise physical measurements to the characterization of photographic papers, and our intended audience includes photograph conservators, curators, and collectors. For our purposes, it is important both to enable fine-grained local comparisons of very similar textures and to identify salient groupings across the entire landscape of photographic papers. The steerable pyramid was developed with the explicit goal of synthesizing textures; as such, it is an overcomplete representation. Its high native dimensionality is an advantage for highly precise local comparisons of texture, as in nearest neighbor search; it is, however, a disadvantage for grouping tasks, which require a well-structured global topology. For grouping tasks, we describe a number of compression and grouping approaches designed to meet a variety of analytical needs. Finally, we validate the steerable pyramid descriptor numerically using ‘one versus all’ classification on an augmented data set that includes multiple samples per paper type.

1 The aim of the present work is to assess similarities between papers used in a collection of 215 lithographs by Henri Matisse. It combines the use of an anisotropic multiscale analysis built upon the hyperbolic wavelet transform to compute distances between textures, with the fact that four textures of the same print were digitized, corresponding to four different (recto/verso and top/side) views. A permutation algorithm is devised showing the importance of reordering the four views to achieve a meaningful comparison across the papers, thus suggesting possibly different artistic intentions in some prints.

Summary Surface textures of a large collection of photographic papers dating from 1896 to the present were measured using a laser scanning confocal microscope (LSCM) with four different objective lenses. Roughness characterization parameters were calculated from the texture measurements and were compared with gloss measurements. Characterization by the area-scale fractal dimension (Das) and the area-scale fractal complexity (Asfc) provided the strongest correlations between gloss reflectance and surface texture. The measurements with the 5× and 10× objectives, which contained many large-scale, spiky measurement artifacts that distorted the measurement, resulted in the strongest correlations (R2 > 0.8) compared to the 20× and 50× (R2

Surface texture is a critical feature in the manufacture, marketing, and use of photographic paper. Raking light reveals texture through a stark rendering of highlights and shadows. Though close-up raking light images effectively document surface features of photographic paper, the sheer number and diversity of textures used for historic papers prohibits efficient visual classification. This work provides evidence that automatic, computer-based classification of texture documented with raking light is feasible by demonstrating an encouraging degree of success sorting a set of 120 images made from samples of historic silver gelatin paper. Using this dataset, four university teams applied different image-processing strategies for automatic feature extraction and degree of similarity quantification.All four approaches successfully detected strong affinities and outliers built into the dataset. The creation and deployment of the algorithms was carried out by the teams without prior knowledge of the distributions of similarities and outliers.These results indicate that automatic classification of silver gelatin photographic paper based on close-up texture images is feasible and should be pursued. To encourage the development of other classification schemes, the 120-sample “training” dataset used in this work is available to other academic researchers at http://www.PaperTextureID.org.

International audience; Wove paper, made on a papermaking screen or mold having a surface of smooth tightly woven wires, was the predominant paper type used for printing in the twentieth century. To aid in the study and classification of fine art prints on wove paper, the present work compares the results of five different image processing approaches for characterizing surface texture. Using a collection of popular wove papers, a reference dataset of raking light close-up images was assembled. Five research teams undertook their own processing strategies to detect affinities among the paper samples. Their success in identifying similarity groupings are reported.

Prior work on texture analysis of historic, photographic papers has focused primarily on measures of texture similarity. However, automated grouping or clustering of photographic paper textures in a way that is meaningful to art conservators remains an open problem. In this work a deep learning approach to automated classification is presented, for clusters derived from a human sorting experiment conducted by 19 art conservators and paper experts and subsequently extended through crowd-sourcing. The proposed approach uses a deep convolutional neural network, and results are presented on the performance in automatically classifying images when compared to human experts.

International audience; Texture characterization of photographic papers is likely to provide scholars with valuable information regarding artistic practices. Currently, texture assessment remains mostly based on visual and manual inspections, implying long repetitive tasks prone to inter- and even intra-observer variability. Automated texture characterization and classification procedures are thus important tasks in historical studies of large databases of photographic papers, likely to provide quantitative and reproducible assessments of texture matches. Such procedures may, for instance, produce vital information on photographic prints of uncertain origins. The hyperbolic wavelet transform, because it relies on the use of different dilation factor along the horizontal and vertical axes, permits to construct robust and meaningful multiscale and anisotropic representation of textures. In the present contribution, we explore how unsupervised clustering strategies can be complemented both to assess the significance of extracted clusters and the strength of the contribution of each texture to its associated cluster. Graph based filterbank strategies are notably investigated with the aim to produce small size significant clusters. These tools are illustrated at work on a large database of about 2500 exposed and non exposed photographic papers carefully assembled and documented by the MoMA and P. Messier's foundation. Results are commented and interpreted.

International audience; Texture characterization of photographic prints can provide scholars with valuable information regarding photographers? aesthetic intentions and working practices. Currently, texture assessment is strictly based on the visual acuity of a range of scholars associated with collecting institutions, such as museum curators and conservators. Natural interindividual discrepancies, intraindividual variability, and the large size of collections present a pressing need for computerized and automated solutions for the texture characterization and classification of photographic prints. In the this article, this challenging image processing task is addressed using an anisotropic multiscale representation of texture, the hyperbolic wavelet transform (HWT), from which robust multiscale features are constructed. Cepstral distances aimed at ensuring balanced multiscale contributions are computed between pairs of images. The resulting large-size affinity matrix is then clustered using spectral clustering, followed by a Ward linkage procedure. For proof of concept, these procedures are first applied to a reference data set of historic photographic papers that combine several levels of similarity and second to a large data set of culturally valuable photographic prints held by the Museum of Modern Art in New York. The characterization and clustering results are interpreted in collaboration with art scholars with an aim toward developing new modes of art historical research and humanities-based collaboration.

Digital imaging and signal processing technologies offer new, quantitative methods to classify and characterize surface textures. This paper presents a collaborative project to systematically and semi-automatically characterize the surface texture of traditional black and white photographic paper as well as contemporary photographic inkjet printing media. Surface texture is a critical feature in the manufacture, marketing and use of photographic papers, especially those used for fine art printing. Raking light reveals texture through a stark rendering of highlights and shadows. Though raking light images effectively document surface features of paper, the sheer number and diversity of textures prohibits efficient visual classification. This work provides evidence that automatic, computer-based classification of texture documented with raking light is feasible by demonstrating an encouraging degree of success sorting two sets of 120 close-up images made from diverse samples of inkjet paper and canvas available in the market from 2000 through 2011 as well as historic samples of black and white paper made at different times during the 20th century The samples used for this study were drawn from two prominent reference collections of photographic media: the Wilhelm Analog and Digital Color Print Materials Reference Collection and Paul Messier's reference collection of historic black and white (silver gelatin) papers. Using these two datasets, four university teams applied different image processing strategies for automatic feature extraction and degree of similarity quantification. All four approaches were successful in detecting strong affinities among similarity groupings built into the datasets as well as identifying outliers. The creation and deployment of the algorithms was carried out by the teams without prior knowledge of the distributions of similarities and outliers. These results indicate that automatic classification of photographic paper based on texture images is feasible. To encourage the development of additional classification schemes, the two “training” datasets used in this work (comprising 240 images) is available to other academic researchers at www.PaperTextureID.org.)

This paper presents a methodology for the examination and documentation of a videobased installation as applied to Dara Birnbaum's 1989–90 Tiananmen Square: Break-In Transmission. Functional and aesthetic roles of compositional elements and electronic components are characterized. Background on relevant technologies is summarized. The results of the assessment indicate that the stability of the piece may be threatened due to media degradation, format obsolescence, hardware obsolescence, and a lack of centralized documentation. Recommendations for near-term and long-term preservation activities are offered. This article is a result of TechArchaeology: A Symposium on Installation Art Preservation.

Daguerreotypes were the first form of photographs and were popular between 1840 and 1860, after which they were superseded by more modern techniques. The daguerreotype image is composed of silver/mercury microcrystals of varying size and density on a silver-coated copper substrate. Nineteenth century daguerreotypes, over the intervening 140 years, have suffered degradation and oxidation, which has greatly reduced their historic and artistic value. Laser ablation techniques have been previously explored for use in the characterization, dating, and restoration of historic paintings, parchments, stained glasses, and statues. We report here the use of a number of modern surface science techniques (especially those using lasers, mass spectrometry, and microscopy) to characterize and analyze both normal and degraded daguerreotypes. Then, attempts to use laser ablation techniques for cleaning and restoring damaged nineteenth century samples will be described. The optimal wavelength, pulse length, pulse energy, and focussing conditions are critical for effective cleaning while preventing damage to the fragile image.

Laser ablation mass spectrometry and surface-enhanced Raman spectroscopy have been used to characterize both modern and 150 year old daguerreotypes. Such investigations are a necessary prelude to attempts to clean them of tarnish by laser ablation. A demonstration of the successful cleaning of a damaged daguerreotype is presented.