Search

Your search keyword '"Paul Messier"' showing total 12 results
Start Over Author "Paul Messier" Language english
12 results on '"Paul Messier"'

1. The Watermark Imaging System: Revealing the Internal Structure of Historical Papers

Author

Elisa Ou, Paul Messier, Ruixue Lian, Andrew Messier, and William Sethares

Subjects
watermark, chain line, image registration, image stack, raking light, transmitted light, Archaeology, CC1-960
Abstract

This paper introduces the Watermark Imaging System (WImSy) which can be used to photograph, document, and study sheets of paper. The WImSy provides surface images, raking light images, and transmitted light images of the paper, all in perfect alignment. We develop algorithms that exploit this alignment by combining several images together in a process that mimics both the “surface image removal” technique and the method of “high dynamic range” photographs. An improved optimization criterion and an automatic parameter selection procedure streamline the process and make it practical for art historians and conservators to extract the relevant information to study watermarks. The effectiveness of the method is demonstrated in several experiments on images taken with the WImSy at the Metropolitan Museum of Art in New York and at the Getty Museum in Los Angeles, and the results are compared with manually optimized images.

Published
2023
Full Text
View/download PDF

2. Bill Brandt: Photography and the Printed Page

Author

Martina Droth, Paul Messier, Richard Caspole, and Robert Hixon

Subjects
Fine Arts, Arts in general, NX1-820
Abstract

This cover collaboration explores the photography of Bill Brandt (1904–1983) from the perspective of the physical print, drawing attention to its material qualities and practical functions. The small sampling presented here captures some of Brandt’s major visual themes, beginning with his socio-political images of wartime Britain through to his more subjective engagement with landscape and art in the post-war decades. This shift is often seen in terms of Brandt’s career path—from photojournalist in the 1930s and 1940s, to art world photographer in the post-war era. Brandt’s stature as an artist came onto assured footing in the 1960s, with exhibitions at the Museum of Modern Art, New York, in 1961 and 1969, and commercial representation by Marlborough Gallery in the 1970s.1 While Brandt began to print to meet these new expectations, he did not abandon the long-standing practices and interests of his early career: his commitment to the reproduction of photographs on the printed page. Newsprint illustrations and bookplates remained his primary expressive channels. Between 1936 and the year of his death, Brandt published at least one photo-book per decade, as well as contributing dozens of photo-essays and hundreds of individual photographs to illustrated magazines.

Published
2020
Full Text
View/download PDF

3. The Watermark Imaging System: Revealing the Internal Structure of Historical Papers

Author

Sethares, Elisa Ou, Paul Messier, Ruixue Lian, Andrew Messier, and William

Subjects
watermark, chain line, image registration, image stack, raking light, transmitted light, image denoising
Abstract

This paper introduces the Watermark Imaging System (WImSy) which can be used to photograph, document, and study sheets of paper. The WImSy provides surface images, raking light images, and transmitted light images of the paper, all in perfect alignment. We develop algorithms that exploit this alignment by combining several images together in a process that mimics both the “surface image removal” technique and the method of “high dynamic range” photographs. An improved optimization criterion and an automatic parameter selection procedure streamline the process and make it practical for art historians and conservators to extract the relevant information to study watermarks. The effectiveness of the method is demonstrated in several experiments on images taken with the WImSy at the Metropolitan Museum of Art in New York and at the Getty Museum in Los Angeles, and the results are compared with manually optimized images.

Published
2023
Full Text
View/download PDF

4. A review of RTI and an investigation into the applicability of micro-RTI as a tool for the documentation and conservation of modern and contemporary paintings

Author

Christina Young, Molly Hughes-Hallett, and Paul Messier

Subjects
Painting, 060102 archaeology, Laser scanning, 010401 analytical chemistry, Museology, 06 humanities and the arts, Conservation, 01 natural sciences, 0104 chemical sciences, Dome (geology), Documentation, Computer graphics (images), 0601 history and archaeology, Polynomial texture mapping, Geology
Abstract

Reflectance Transformation Imaging (RTI) is a non-invasive method of examination which can be used to document and visualize the surface texture of objects, including artworks. Through imaging an object under different directions of illumination, topographical maps of a low relief surface can be produced, which when further processed can be used as an interactive visual aid. This paper summarizes the current state of research on micro-RTI, and presents an investigation into a micro-RTI system comprised of a digital microscope and miniature lighting array dome, which was originally developed to study small areas of surface texture. The article reviews the techniques required to adequately use the micro-RTI, and evaluates the system’s use as a practical tool for conserving and documenting modern and contemporary easel paintings. This was determined by evaluating its data processing, ability to accurately document test panels and case study paintings, and comparing it with alternative documentation methods of standard microscopy and laser scanning. It was found that micro-RTI is a useful tool for modern and contemporary paintings and their often complex and/or delicate surfaces, where subtle surface alterations can result in visually distracting features.

Published
2021

5. Performance of two multiscale texture algorithms in classifying silver gelatine paper via k-nearest neighbors

Author

Herwig Wendt, Andrew G. Klein, Kirsten R. Basinet, Patrice Abry, Paul Messier, Stéphane Roux, Centre National de la Recherche Scientifique - CNRS (FRANCE), Ecole Normale Supérieure de Lyon - ENS de Lyon (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université Claude Bernard-Lyon I - UCBL (FRANCE), Université de Lyon - UDL (FRANCE), Western Washington University - WWU (USA), Yale University (USA), Laboratoire de Physique (Lyon, France), Western Washington University (WWU), Laboratoire de Physique de l'ENS Lyon (Phys-ENS), École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, CoMputational imagINg anD viSion (IRIT-MINDS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Department of Electrical Engineering, College of Engineering, Qatar University, PO BOX 2713, Doha, Qatar, Qatar University, École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), and Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)

Subjects
Photographic paper, Computer science, 020206 networking & telecommunications, Context (language use), 02 engineering and technology, Intelligence artificielle, k-nearest neighbors algorithm, Visualization, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Data set, Texture similarity, Statistical classification, Similarity (network science), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Crowd-sourcing, Algorithm, Multiscale analysis
Abstract

International audience; As part of the Historic Photographic Paper Classification Challenge, a multitude of approaches to quantifying paper texture similarity have been developed. These approaches have yielded encouraging results when applied to very controlled datasets containing photomicrographs of familiar specimens. In this paper, we report on the k-nearest neighbors classification performance of two multiscale analysis-based texture similarity approaches when applied to a much larger reference collection of silver gelatin photographic papers. The clusters for this data set were derived from a visual sorting experiment conducted by art conservators and paper experts later extended through crowd-sourcing. The results show that these texture similarity approaches, when combined with a simple k-nearest neighbors classification algorithm, yield workable performances with accuracy of up to 69%. We discuss this outcome in the context of available data and the cross-validation procedure used, then provide suggestions for improvement.

Published
2018

6. Multiscale Anisotropic Texture Unsupervised Clustering for Photographic Paper

Author

Stéphane Roux, Pierre Borgnat, Paul Messier, Patrice Abry, Nicolas Tremblay, Herwig Wendt, Centre National de la Recherche Scientifique - CNRS (FRANCE), Ecole Normale Supérieure de Lyon - ENS de Lyon (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université Claude Bernard-Lyon I - UCBL (FRANCE), Paul Messier (USA), Laboratoire de Physique de l'ENS Lyon (Phys-ENS), École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Signal et Communications (IRIT-SC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Chercheur indépendant, Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), and Université de Toulouse (UT)

Subjects
Stability criteria, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Traitement des images, Databases, Wavelet transforms, Image texture, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Texture filtering, Traitement du signal et de l'image, Computer vision, Laplace equations, Anisotropy, Synthèse d'image et réalité virtuelle, ComputingMethodologies_COMPUTERGRAPHICS, business.industry, Wavelet transform, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Vision par ordinateur et reconnaissance de formes, Intelligence artificielle, Filter bank, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Kernel, Kernel (image processing), [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Artificial intelligence, Unsupervised clustering, business, Art, Photographic paper
Abstract

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.

Published
2015

7. Wove Paper Analysis through Texture Similarities

Author

David L. Neuhoff, Margaret Holben Ellis, Yuanhao Zhai, Andrew G. Klein, Stéphane Roux, Paul Messier, Herwig Wendt, William A. Sethares, Patrice Abry, David Picard, Chris R. Johnson, Stéphane Jaffard, Laboratoire de Physique de l'ENS Lyon (Phys-ENS), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Western Washington University (WWU), Yale University [New Haven], New York University [New York] (NYU), NYU System (NYU), University of Wisconsin-Madison, Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA), University of Michigan [Ann Arbor], University of Michigan System, Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, Laboratoire d'Analyse et de Mathématiques Appliquées (LAMA), Université Paris-Est Marne-la-Vallée (UPEM)-Fédération de Recherche Bézout-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Cornell University, École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Signal et Communications (IRIT-SC), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Fédération de Recherche Bézout-Université Paris-Est Marne-la-Vallée (UPEM), Cornell University [New York], Centre National de la Recherche Scientifique - CNRS (FRANCE), Cornell University (USA), Ecole Normale Supérieure de Lyon - ENS de Lyon (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université Paris Est Créteil Val de Marne - UPEC (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université de Cergy-Pontoise (FRANCE), Université Claude Bernard-Lyon I - UCBL (FRANCE), Université de Lyon - UDL (FRANCE), University of Michigan - U-M (USA), University of Wisconsin - Madison (USA), Western Washington University - WWU (USA), Ecole Nationale Supérieure de l'Electronique et de ses Applications - ENSEA (Cergy-Pontoise, France), Institut de Recherche en Informatique de Toulouse - IRIT (Toulouse, France), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), and Université de Toulouse (UT)

Subjects
Engineering drawing, Engineering, Image classification, Feature extraction, Image processing, 02 engineering and technology, Texture (music), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Traitement des images, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Traitement du signal et de l'image, Weaving, Surface texture, Synthèse d'image et réalité virtuelle, Information retrieval, Contextual image classification, business.industry, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, Vision par ordinateur et reconnaissance de formes, Intelligence artificielle, Woven composites, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Fine art, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Printing, 020201 artificial intelligence & image processing, business, Reference dataset
Abstract

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.

Published
2016

8. Multiscale Anisotropic Texture Analysis and Classification of Photographic Prints: Art scholarship meets image processing algorithms

Author

Béatrice Vedel, Andrew G. Klein, Stéphane Jaffard, Pierre Borgnat, Paul Messier, Jim Coddington, Patrice Abry, Stéphane Roux, Nicolas Tremblay, Herwig Wendt, Lee Ann Daffner, Centre National de la Recherche Scientifique - CNRS (FRANCE), Ecole Normale Supérieure de Lyon - ENS de Lyon (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Paris Est Créteil Val de Marne - UPEC (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université Claude Bernard-Lyon I - UCBL (FRANCE), The Museum of Modern Art - MoMA (USA), Université de Bretagne Sud - UBS (FRANCE), Western Washington University - WWU (USA), Yale University (USA), Laboratoire d'Analyse et de Mathématiques Appliquées - LAMA (Paris, France), Laboratoire de Physique de l'ENS Lyon (Phys-ENS), École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Signal et Communications (IRIT-SC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Institute for the Preservation of Cultural Heritage, Yale University [New Haven], Western Washington University (WWU), Laboratoire d'Analyse et de Mathématiques Appliquées (LAMA), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Fédération de Recherche Bézout-Université Paris-Est Marne-la-Vallée (UPEM), Université de Bretagne Sud - Vannes (UBS Vannes), Université de Bretagne Sud (UBS), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Université Paris-Est Marne-la-Vallée (UPEM)-Fédération de Recherche Bézout-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Institut National Polytechnique de Toulouse - INPT (FRANCE), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP)

Subjects
Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Image processing, 02 engineering and technology, Texture (music), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Traitement des images, Image texture, Modern art, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Digital image processing, 0202 electrical engineering, electronic engineering, information engineering, Photography, Traitement du signal et de l'image, Computer vision, Electrical and Electronic Engineering, Cluster analysis, Surface texture, Synthèse d'image et réalité virtuelle, ComputingMethodologies_COMPUTERGRAPHICS, Information retrieval, business.industry, Applied Mathematics, Anisotropic magnetoresistance, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, Vision par ordinateur et reconnaissance de formes, Intelligence artificielle, Spectral clustering, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Optical surface waves, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Signal Processing, 020201 artificial intelligence & image processing, Artificial intelligence, business, Art
Abstract

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.

Published
2015

9. Automated Surface Texture Classification of Inkjet and Photographic Media

Author

Messier, P., Johnson, R., Wilhelm, H., Sethares, W. A., Klein, A. G., Patrice Abry, Jaffard, S., Wendt, H., Roux, S., Pustelnik, N., Noord, N., Maaten, L. D., Postma, E., Centre National de la Recherche Scientifique - CNRS (FRANCE), Cornell University (USA), Ecole Normale Supérieure de Lyon - ENS de Lyon (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Paris Est Créteil Val de Marne - UPEC (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Paul Messier (USA), Tilburg University (NETHERLANDS), University of Wisconsin - Madison (USA), Wilhem imaging research (USA), Worcester Polytechnic Institute - WPI (USA), Institut de Recherche en Informatique de Toulouse - IRIT (Toulouse, France), Paul Messier LLC, Cornell University [New York], University of Wisconsin-Madison, Worcester Polytechnic Institute, École normale supérieure de Lyon (ENS de Lyon), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), CoMputational imagINg anD viSion (IRIT-MINDS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Centre National de la Recherche Scientifique (CNRS), Tilburg University [Tilburg], and Netspar

Subjects
Traitement des images, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Traitement du signal et de l'image, Computer vision, Vision par ordinateur et reconnaissance de formes, Intelligence artificielle, Synthèse d'image et réalité virtuelle, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Digital imaging and signal processing technologies offer new methods for inkjet and photographic media engineers and manufacturers, and those responsible for product quality control, to classify and characterize printing materials surface textures using new and more quantitative methods. This paper presents a collaborative project to systematically and semi-automatically characterize the surface texture of inkjet media. These methods have applications in product design and specification, and in manufacturing quality control. Surface texture is a critical feature in the manufacture, marketing and use of inkjet papers, especially those used for fine art printing. Raking light reveals texture through a stark rendering of highlights and shadows. Though raking light photomicrographs effectively document surface features of inkjet 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 photomicrographs is feasible by demonstrating an encouraging degree of success sorting a set of 120 photomicrographs made from diverse samples of inkjet paper and canvas available in the market from 2000 through 2011. The samples used for this study were drawn from the Wilhelm Analog and Digital Color Print Materials Reference Collection. Using this dataset, 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 dataset 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 inkjet paper based on texture photomicrographs is feasible. To encourage the development of additional classification schemes, the 120 inkjet sample “training” dataset used in this work is available to other academic researchers at www.PaperTextureID.org.

Published
2013

10. Texture classification of photographic papers: improving spectral clustering using filterbanks on graphs

Author

Tremblay, Nicolas, Roux, Stéphane g., Borgnat, Pierre, Abry, Patrice, Wendt, Herwig, Messier, Paul, Laboratoire de Physique de l'ENS Lyon (Phys-ENS), École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Paul Messier (USA), Signal et Communications (IRIT-SC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Chercheur indépendant, Centre National de la Recherche Scientifique - CNRS (FRANCE), Ecole Normale Supérieure de Lyon - ENS de Lyon (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Institut de Recherche en Informatique de Toulouse - IRIT (Toulouse, France), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), and Institut National Polytechnique de Toulouse - INPT (FRANCE)

Subjects
Community detection, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Vision par ordinateur et reconnaissance de formes, Intelligence artificielle, Art historian, Print, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Traitement des images, ComputingMethodologies_PATTERNRECOGNITION, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Spectral clustering, Traitement du signal et de l'image, Filterbank, Synthèse d'image et réalité virtuelle
Abstract

International audience; From the point of view of graph signal processing, we show that spectral clustering is equivalent to an ideal low-pass filterbank. Building upon previous multiscale community detection ideas [11], and integrating the concept of community cores [8], we propose a data-driven filterbank-based classification method. We apply this method to the texture classification of photographic papers useful to art historians, and we show that it provides a richer and more informative description of the data’s structure in clusters.

Published
2015

11. Pursuing automated classification of historic photographic papers from raking light photomicrographs

Author

Johnson, Richard, Messier, Paul, Sethares, William A., Klein, Andrew G., Brown, Christopher, Do, Anh Hoang, Klausmeyer, Philip, Abry, Patrice, Jaffard, Stéphane, Wendt, Herwig, Roux, Stéphane, Pustelnik, Nelly, Van Noord, Nanne, Van Der Maaten, Laurens, Postma, Eric, Coddington, Jim, Daffner, Lee Ann, Murata, Hanako, Wilhelm, Henry, Wood, Sally, Messier, Mark, Cornell University [New York], Paul Messier (USA), University of Wisconsin-Madison, Worcester Polytechnic Institute - WPI (USA), Worcester Art Museum - WAM (USA), École normale supérieure - Lyon (ENS Lyon), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), CoMputational imagINg anD viSion (IRIT-MINDS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Tilburg University [Netherlands], Delft University of Technology (TU Delft), MoMA Srl, Wilhem imaging research (USA), Santa Clara University, Indiana University [Bloomington], Indiana University System, École normale supérieure de Lyon (ENS de Lyon), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Centre National de la Recherche Scientifique - CNRS (FRANCE), Cornell University (USA), Ecole Normale Supérieure de Lyon - ENS de Lyon (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Paris Est Créteil Val de Marne - UPEC (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Delft University of Technology - TU Delft (NETHERLANDS), Indiana University - IU (USA), Santa Clara University - SCU (USA), The Museum of Modern Art - MoMA (USA), Tilburg University (NETHERLANDS), University of Wisconsin - Madison (USA), Institut de Recherche en Informatique de Toulouse - IRIT (Toulouse, France), and Creative Computing

Subjects
Texture classification, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Vision par ordinateur et reconnaissance de formes, Historic photographic paper, Intelligence artificielle, Area-scale analysis, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Traitement des images, Texture analysis, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Random-feature texton, Traitement du signal et de l'image, Art investigation, Synthèse d'image et réalité virtuelle, Multiscale analysis
Abstract

International audience; 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.; La textura superficial es un rasgo crítico en la manufactura, comercialización y uso de papel fotográfico. La luz rasante revela la textura a través de una representación nítida de áreas luminosas y sombras. A pesar de que las imágenes con luz rasante documentan efectivamente las características superficiales del papel fotográfico, la cantidad misma y la diversidad de texturas usadas en papeles históricos hace imposible una clasificación visual eficiente. Este trabajo proporciona evidencias de que la clasificación automática de la textura documentada con luz rasante, realizada por computadora es factible al demostrar un motivante grado de éxito al clasificar un grupo de 120 imágenes hechas de muestras de papel histórico de gelatina de plata. Usando esta base de datos, cuatro equipos universitarios aplicaron diferentes estrategias de procesamiento de imágenes para la extracción automática de rasgos y cuantificación del grado de similitud. Las cuatro estrategias detectaron con éxito afinidades fuertes así como los casos atípicos incluidos en la base de datos. Los equipos crearon y usaron algoritmos sin conocimiento previo de las similitudes y los casos lejanos al promedio. Estos resultados indican que la clasificación automática de papel fotográfico de gelatina de plata basada en imágenes que muestran acercamientos de la textura es viable y debería realizarse. Para incentivar el desarrollo de otros esquemas de clasificación el grupo de 120 muestras de “entrenamiento” usado en este trabajo está disponible para otros investigadores en http://www.PaperTextureID.org.; La texture de la surface du papier photographique est une caractéristique essentielle de sa fabrication, de sa commercialisation et de son utilisation. La lumière rasante révèle la texture du papier par un rendu saisissant des lumières et des ombres. Bien que les gros plans en lumière rasante documentent efficacement les caractéristiques de la surface des papiers photographiques, le nombre et la diversité des textures des papiers historiques empêchent une classification visuelle efficace. Le présent travail démontre que la classification automatique par ordinateur de textures documentées avec une lumière rasante est possible, en démontrant un degré encourageant de succès dans le tri d'un ensemble de 120 images réalisées à partir d'échantillons de papiers historiques au gélatino-bromure d'argent. À partir de ce groupe d'images, quatre équipes universitaires ont appliqué différentes stratégies de traitement d'image afin d'extraire automatiquement des caractéristiques et de quantifier le degré de similitude des papiers. Les quatre approches ont chacune réussi à détecter de fortes affinités, ainsi que des images avec caractéristique aberrante intégrées dans le groupe. La création et le déploiement des algorithmes ont été réalisés par les équipes sans connaissance préalable de la répartition des similitudes et des images aberrantes. Ces résultats indiquent que la classification automatique des papiers photographiques au gélatino-bromure d'argent basée sur des gros plans de texture est possible et doit être poursuivie. Pour encourager le développement d'autres systèmes de classification, les données des 120 échantillons utilisées dans ce travail sont mises à la disposition d'autres chercheurs scientifiques à l'adresse suivante: http://www.PaperTextureID.org.

Published
2014

12. Correlation between gloss reflectance and surface texture in photographic paper.

Author

Vessot K, Messier P, Hyde JM, and Brown CA

Abstract

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 (R(2)  > 0.8) compared to the 20× and 50× (R(2)  < 0.5). The presence of spiky artifacts in the measurements, which increases when the magnification of the objective lens is decreased, appears to amplify surface features in such a way to improve the correlations., (© Wiley Periodicals, Inc.)

Published
2015
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results