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664 results on '"Messinger, David W."'

1. Gaussian Blur and Relative Edge Response

Author

Bergstrom, Austin C., Conran, David, and Messinger, David W.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Physics - Optics
Abstract

It is often convenient to use Gaussian blur in studying image quality or in data augmentation pipelines for training convoluional neural networks. Because of their convenience, Guassians are sometimes used as first order approximations of optical point spread functions. Here, we derive and evaluate closed form relationships between Gaussian blur parameters and relative edge response, finding good agreement with measured results. Additionally, we evaluate the extent to which Gaussian approximations of optical point spread functions can be used to predict relative edge response, finding that Gaussian relationships provide a reasonable approximation in limited circumstances but not across a wide range of optical parameters.

Published
2023

4. Hyperspectral Pigment Analysis of Cultural Heritage Artifacts Using the Opaque Form of Kubelka-Munk Theory

Author

Taufique, Abu Md Niamul and Messinger, David W.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Kubelka-Munk (K-M) theory has been successfully used to estimate pigment concentrations in the pigment mixtures of modern paintings in spectral imagery. In this study the single-constant K-M theory has been utilized for the classification of green pigments in the Selden Map of China, a navigational map of the South China Sea likely created in the early seventeenth century. Hyperspectral data of the map was collected at the Bodleian Library, University of Oxford, and can be used to estimate the pigment diversity, and spatial distribution, within the map. This work seeks to assess the utility of analyzing the data in the K/S space from Kubelka-Munk theory, as opposed to the traditional reflectance domain. We estimate the dimensionality of the data and extract endmembers in the reflectance domain. Then we perform linear unmixing to estimate abundances in the K/S space, and following Bai, et al. (2017), we perform a classification in the abundance space. Finally, due to the lack of ground truth labels, the classification accuracy was estimated by computing the mean spectrum of each class as the representative signature of that class, and calculating the root mean squared error with all the pixels in that class to create a spatial representation of the error. This highlights both the magnitude of, and any spatial pattern in, the errors, indicating if a particular pigment is not well modeled in this approach., Comment: 11 pages, 9 figures

Published
2021
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6. Deep Learning Spatial-Spectral Processing of Hyperspectral Images for Pigment Mapping of Cultural Heritage Artifacts

Author

Bai, Di, Messinger, David W., Howell, David, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Del Bimbo, Alberto, editor, Cucchiara, Rita, editor, Sclaroff, Stan, editor, Farinella, Giovanni Maria, editor, Mei, Tao, editor, Bertini, Marco, editor, Escalante, Hugo Jair, editor, and Vezzani, Roberto, editor

Published
2021
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45. Additional file 1 of An alternative approach to mapping pigments in paintings with hyperspectral reflectance image cubes using artificial intelligence

Author

Kleynhans, Tania, Patterson, Catherine M. Schmidt, Dooley, Kathryn A., Messinger, David W., and Delaney, John K.

Abstract

Additional file 1: Figure S1. The four paintings from the Laudario used to create the training dataset for the 1D-CNN model. a The Nativity with the Annunciation to the Shepherds, Master of the Dominican Effigies, c. 1340, miniature on vellum, National Gallery of Art, Washington, D.C., Rosenwald Collection, 1949.5.87, b The Ascension of Christ, Pacino di Bonaguida, about 1340, Tempera and gold leaf on parchment. Getty Museum, Los Angeles, Ms. 80a (2005.26), verso, c The Martyrdom of Saint Lawrence, Pacino di Bonaguida, about 1340, Tempera and gold leaf on parchment. Getty Museum, Los Angeles, Ms. 80b(2006.13), verso ( d) Christ and the Virgin Enthroned with Forty Saints, Master of the Dominican Effigies, c.1340, miniature on vellum, National Gallery of Art, Washington, D.C., Rosenwald Collection, 1959.16.2. Digital images of (b), (c). Table S1. Summary of per class accuracy for the 1D-CNN model. The 25 pigment/mixture classes and their total class size is given along with the per class accuracy using 10-fold cross validation.) courtesy of the Getty's Open Content Program. Figure S2. The average reectance spectra for each of the pigment labeled classes in the training dataset used for the 1D-CNN model. Figure S3. The pigment labeled map created by using a lower threshold (0.85) in the 1D-CNN. Table S2. Summary of analyses, the Pentecost. Note that the RIS features listed are those identified by an expert user following manual data exploration. When available, fiber optic reflectance spectroscopy (FORS) and Raman analysis may provide additional information about the total chemical composition of each area. However, not all pigments identified are discernible in the RIS data cube on which the 1D-CNN is applied. Therefore, a simplified “pigment class” column notes the materials that should be identified in the paint by this technique. Table S3. Summary of analyses, Saint Peter Enthroned. Note that the RIS features listed are those identified by an expert user following manual data exploration. When available, fiber optic reflectance spectroscopy (FORS) analysis may provide additional information about the total chemical composition of each area. However, not all pigments identified are discernible in the RIS data cube on which the 1D-CNN is applied. Therefore, a simplified “pigment class” column notes the materials that should be identified in the paint by this technique.

Published
2020
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