scholarly journals Reflectance Imaging Spectroscopy (RIS) for Operation Night Watch: Challenges and Achievements of Imaging Rembrandt’s Masterpiece in the Glass Chamber at the Rijksmuseum

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6855
Author(s):  
Francesca Gabrieli ◽  
John K. Delaney ◽  
Robert G. Erdmann ◽  
Victor Gonzalez ◽  
Annelies van van Loon ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Color Image ◽  
Imaging Spectroscopy ◽  
High Sensitivity ◽  
Data Sets ◽  
Paint Sample ◽  
Large Size ◽  
Conservation Plan ◽  
Reflectance Imaging Spectroscopy ◽  
Invasive Techniques

Visible and infrared reflectance imaging spectroscopy is one of the several non-invasive techniques used during Operation Night Watch for the study of Rembrandt’s iconic masterpiece The Night Watch (1642). The goals of this project include the identification and mapping of the artists’ materials, providing information about the painting technique used as well as documenting the painting’s current state and ultimately determining the possible conservation plan. The large size of the painting (3.78 m by 4.53 m) and the diversity of the technical investigations being performed make Operation Night Watch the largest research project ever undertaken at the Rijksmuseum. To construct a complete reflectance image cube at a high spatial resolution (168 µm2) and spectral resolution (2.54 to 6 nm), the painting was imaged with two high-sensitivity line scanning hyperspectral cameras (VNIR 400 to 1000 nm, 2.54 nm, and SWIR 900 to 2500 nm, 6 nm). Given the large size of the painting, a custom computer-controlled 3-D imaging frame was constructed to move each camera, along with lights, across the painting surface. A third axis, normal to the painting, was added along with a distance-sensing system which kept the cameras in focus during the scanning. A total of 200 hyperspectral image swaths were collected, mosaicked and registered to a high-resolution color image to sub-pixel accuracy using a novel registration algorithm. The preliminary analysis of the VNIR and SWIR reflectance images has identified many of the pigments used and their distribution across the painting. The SWIR, in particular, has provided an improved visualization of the preparatory sketches and changes in the painted composition. These data sets, when combined with the results from the other spectral imaging modalities and paint sample analyses, will provide the most complete understanding of the materials and painting techniques used by Rembrandt in The Night Watch.

Independent macroscopic chemical mappings of cultural heritage materials with reflectance imaging spectroscopy: case study of a 16thcentury Aztec manuscript

2017 ◽  
Vol 9 (42) ◽  
pp. 5997-6008 ◽  
Author(s):  
Fabien Pottier ◽  
Salomon Kwimang ◽  
Anne Michelin ◽  
Christine Andraud ◽  
Fabrice Goubard ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Data Processing ◽  
Hyperspectral Image ◽  
Imaging Spectroscopy ◽  
Spectral Feature ◽  
Image Data ◽  
Painting Materials ◽  
Hyperspectral Image Data ◽  
Reflectance Imaging Spectroscopy

Hyperspectral image data processing based on specific spectral feature extraction protocols allows mapping of historical painting materials independently.

Use of near infrared reflectance imaging spectroscopy to map wool and silk fibres in historic tapestries

2016 ◽  
Vol 8 (44) ◽  
pp. 7886-7890 ◽  
Author(s):  
John K. Delaney ◽  
Paola Ricciardi ◽  
Lisha Glinsman ◽  
Michael Palmer ◽  
Julia Burke
Keyword(s):  
Near Infrared ◽  
Imaging Spectroscopy ◽  
High Sensitivity ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Textile Fibres ◽  
Reflectance Imaging Spectroscopy ◽  
Hyperspectral Camera

Reflectance imaging spectroscopy is examined as a tool to map and identify natural textile fibresin situon historic tapestries using a high-sensitivity hyperspectral camera.

scholarly journals A SIFT Description Approach for Non-Uniform Illumination and Other Invariants

2021 ◽  
Vol 26 (6) ◽  
pp. 533-539
Author(s):  
Krittachai Boonsivanon ◽  
Worawat Sa-Ngiamvibool
Keyword(s):  
Color Image ◽  
High Sensitivity ◽  
Data Sets ◽  
Morphological Operations ◽  
Uniform Illumination ◽  
Data Set ◽  
Keypoint Detection ◽  
Accuracy And Precision ◽  
Particle Images ◽  
Precision Rate

The new improvement keypoint description technique of image-based recognition for rotation, viewpoint and non-uniform illumination situations is presented. The technique is relatively simple based on two procedures, i.e., the keypoint detection and the keypoint description procedure. The keypoint detection procedure is based on the SIFT approach, Top-Hat filtering, morphological operations and average filtering approach. Where this keypoint detection procedure can segment the targets from uneven illumination particle images. While the keypoint description procedures are described and implemented using the Hu moment invariants. Where the central moments are being unchanged under image translations. The sensitivity, accuracy and precision rate of data sets were evaluated and compared. The data set are provided by color image database with variants uniform and non-uniform illumination, viewpoint and rotation changes. The evaluative results show that the approach is superior to the other SIFTs in terms of uniform illumination, non-uniform illumination and other situations. Additionally, the paper demonstrates the high sensitivity of 100%, high accuracy of 83.33% and high precision rate of 80.00%. Comparisons to other SIFT approaches are also included.

Proposal of Micro-Trichroic Structures for High-Sensitivity Color Image Sensors

2011 ◽  
Vol 50 (4) ◽  
pp. 042201 ◽  
Author(s):  
Yuta Suzuki ◽  
Yasuyuki Ozeki ◽  
Tomoki Yoshino ◽  
Kazuhiro Yamada ◽  
Michihiro Yamagata ◽  
...  
Keyword(s):  
Color Image ◽  
High Sensitivity ◽  
Image Sensors

scholarly journals Comparison of spectral selection methods in the development of classification models from visible near infrared hyperspectral imaging data

2019 ◽  
Author(s):  
Aoife Gowen ◽  
Jun-Li Xu ◽  
Ana Herrero-Langreo
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Imaging Spectroscopy ◽  
Model Performance ◽  
Data Sampling ◽  
Classification Models ◽  
Imaging Data ◽  
Selection Of

Applications of hyperspectral imaging (HSI) to the quantitative and qualitative measurement of samples have grown widely in recent years, due mainly to the improved performance and lower cost of imaging spectroscopy instrumentation. Data sampling is a crucial yet often overlooked step in hyperspectral image analysis, which impacts the subsequent results and their interpretation. In the selection of pixel spectra for the calibration of classification models, the spatial information in HSI data can be exploited. In this paper, a variety of sampling strategies for selection of pixel spectra are presented, exemplified through five case studies. The strategies are compared in terms of the proportion of global variability captured, practicality and predictive model performance. The use of variographic analysis as a guide to the spatial segmentation prior to sampling leads to the selection of representative subsets while reducing the variation in model performance parameters over repeated random selection.

Sensible use of observational clinical data

2011 ◽  
Vol 22 (1) ◽  
pp. 7-13 ◽  
Author(s):  
J Marc Overhage ◽  
Lauren M Overhage
Keyword(s):  
Health Care ◽  
Clinical Trials ◽  
Medical Research ◽  
Observational Data ◽  
Clinical Data ◽  
Low Cost ◽  
Clinical Care ◽  
Data Sets ◽  
Large Size ◽  
Health Care Operations

Observational data sets offer many potential advantages for medical research including their low cost, large size and generalisability. Because they are collected for clinical care and health care operations purposes, observational data sets have some limitations that must be considered in order to perform useful analyses. Sensible use of observational data sets can yield valuable insights, particularly when clinical trials are impractical.

An Incremental Isomap Method for Hyperspectral Dimensionality Reduction and Classification

2021 ◽  
Vol 87 (6) ◽  
pp. 445-455
Author(s):  
Yi Ma ◽  
Zezhong Zheng ◽  
Yutang Ma ◽  
Mingcang Zhu ◽  
Ran Huang ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Training Data ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Data Points

Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We pres- ent in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature varia- tion algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine.

scholarly journals Kernel Joint Sparse Representation Based on Self-Paced Learning for Hyperspectral Image Classification

2019 ◽  
Vol 11 (9) ◽  
pp. 1114
Author(s):  
Sixiu Hu ◽  
Jiangtao Peng ◽  
Yingxiong Fu ◽  
Luoqing Li
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Feature Space ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Data Sets ◽  
Neighborhood Structure ◽  
Joint Sparse Representation ◽  
Negative Effect ◽  
Sparse Coefficient

By means of joint sparse representation (JSR) and kernel representation, kernel joint sparse representation (KJSR) models can effectively model the intrinsic nonlinear relations of hyperspectral data and better exploit spatial neighborhood structure to improve the classification performance of hyperspectral images. However, due to the presence of noisy or inhomogeneous pixels around the central testing pixel in the spatial domain, the performance of KJSR is greatly affected. Motivated by the idea of self-paced learning (SPL), this paper proposes a self-paced KJSR (SPKJSR) model to adaptively learn weights and sparse coefficient vectors for different neighboring pixels in the kernel-based feature space. SPL strateges can learn a weight to indicate the difficulty of feature pixels within a spatial neighborhood. By assigning small weights for unimportant or complex pixels, the negative effect of inhomogeneous or noisy neighboring pixels can be suppressed. Hence, SPKJSR is usually much more robust. Experimental results on Indian Pines and Salinas hyperspectral data sets demonstrate that SPKJSR is much more effective than traditional JSR and KJSR models.

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