scholarly journals Nondestructive Evaluation of Heritage Object Coatings with Four Hyperspectral Imaging Systems

Coatings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 244
Author(s):  
Jakub Sandak ◽  
Anna Sandak ◽  
Lea Legan ◽  
Klara Retko ◽  
Maša Kavčič ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Protective Coatings ◽  
Imaging Techniques ◽  
Imaging Systems ◽  
Original Paintings ◽  
Specific Objective ◽  
Panel Painting ◽  
Spectral Ranges ◽  
Cultural Heritage Objects ◽  
Original Construction

Advanced imaging techniques can noninvasively characterise, monitor, and evaluate how conservation treatments affect cultural heritage objects. In this specific field, hyperspectral imaging allows nondestructive characterisation of materials by identifying and characterising colouring agents, binders, and protective coatings as components of an object’s original construction or later historic additions. Furthermore, hyperspectral imaging can be used to monitor deterioration or changes caused by environmental conditions. This paper examines the potential of hyperspectral imaging (HSI) for the evaluation of heritage objects. Four cameras operating in different spectral ranges were used to nondestructively scan a beehive panel painting that originated from the Slovene Ethnographic Museum collection. The specific objective of this research was to identify pigments and binders present in the samples and to spatially map the presence of these across the surface of the art piece. Merging the results with databases created in parallel using other reference methods allows for the identification of materials originally used by the artist on the panel. Later interventions to the original paintings can also be traced as part of past conservation campaigns.

scholarly journals Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

2020 ◽  
Vol 10 (3) ◽  
pp. 1173 ◽  
Author(s):  
Zhiqi Hong ◽  
Yong He
Keyword(s):  
Hyperspectral Imaging ◽  
Nutritional Value ◽  
Geographical Origin ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Imaging Systems ◽  
Support Vector ◽  
Spectral Ranges ◽  
Tea Leaf ◽  
Non Destructive

Longjing tea is one of China’s protected geographical indication products with high commercial and nutritional value. The geographical origin of Longjing tea is an important factor influencing its commercial and nutritional value. Hyperspectral imaging systems covering the two spectral ranges of 380–1030 nm and 874–1734 nm were used to identify a single tea leaf of Longjing tea from six geographical origins. Principal component analysis (PCA) was conducted on hyperspectral images to form PCA score images. Differences among samples from different geographical origins were visually observed from the PCA score images. Support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) models were built using the full spectra at the two spectral ranges. Decent classification performances were obtained at the two spectral ranges, with the overall classification accuracy of the calibration and prediction sets over 84%. Furthermore, prediction maps for geographical origins identification of Longjing tea were obtained by applying the SVM models on the hyperspectral images. The overall results illustrate that hyperspectral imaging at both spectral ranges can be applied to identify the geographical origin of single tea leaves of Longjing tea. This study provides a new, rapid, and non-destructive alternative for Longjing tea geographical origins identification.

scholarly journals To Predict the Lightfastness of Prints on Foil Applying Artificial Neural Network

2020 ◽  
Vol 2020 (1) ◽  
pp. 27-32
Author(s):  
Mahasweta Mandal ◽  
Swati Bandyopadhyay
Keyword(s):  
Management Practices ◽  
New Technologies ◽  
Imaging Techniques ◽  
Image Data ◽  
Imaging Systems ◽  
Multiple Sources ◽  
Advanced Imaging ◽  
X Ray ◽  
Multiple Imaging ◽  
Cultural Heritage Objects

Archives, libraries, and commercial firms are utilizing new advanced imaging methods for research into cultural heritage objects. New technical systems, including the latest multispectral (MSI) and x-ray fluorescence (XRF) imaging systems and higher resolution cameras raise major challenges for not only the integration of new technologies, but also the ability to store, manage and access large amounts of data in archives and libraries. Recent advanced imaging of ancient Syriac palimpsests (parchment manuscripts with hidden texts embedded within them) demonstrated an approach that utilized multiple imaging techniques and integration and analysis of data from multiple sources. Three palimpsest imaging projects (Archimedes Palimpsest, Syriac Galen Palimpsest, HMML Palimpsest) supported research with a range of advanced imaging techniques with MSI and XRF, requiring implementation and standardization of new digitization and data management practices for the integration, preservation and sharing of advanced image data.

Geometrical and spectral data stitching for combining hyperspectral imaging systems

2017 ◽  
Vol 65 (6) ◽  
Author(s):  
Fabian Stark ◽  
Maik Rosenberger ◽  
Paul-Gerald Dittrich ◽  
Rafael Celestre ◽  
Michael Hänsel ◽  
...  
Keyword(s):  
Image Registration ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Imaging Systems ◽  
Data Sets ◽  
Correction Algorithm ◽  
Optical Aberrations ◽  
Optical Aberration ◽  
Spectral Ranges

AbstractOne way to increase the amount of information acquired via hyperspectral imaging and therefore to increase the possibility of data analysis is combining the spatial and spectral information of hyperspectral data sets. The aforementioned data sets are obtained by cameras covering different spectral ranges. The purpose of this article is to develop an algorithm which is able to combine two data sets acquired by two hyperspectral pushbroom imagers, covering the visible (VIS) and the near infrared (NIR) wavelength range. Initially, the effect of optical aberrations, as well as errors via the image registration were examined. Subsequently a correction algorithm for both the optical aberration and the image registration is elaborated.

scholarly journals Non-destructive and rapid discrimination of hard-to-cook beans using hyperspectral imaging

2018 ◽  
pp. 7.1-7.8
Author(s):  
Lina Diaz-Contreras ◽  
Chyngyz Erkinbaev ◽  
Jitendra Paliwal
Keyword(s):  
Hyperspectral Imaging ◽  
Nutritional Value ◽  
Near Infrared ◽  
Storage Conditions ◽  
Imaging Systems ◽  
Electromagnetic Spectrum ◽  
Rapid Discrimination ◽  
Spectral Ranges ◽  
And Control ◽  
Non Destructive

Dry beans stored under sub-optimal conditions tend to develop hard-to-cook (HTC) defect, which extends the cooking time making them less palatable while reducing their nutritional value. The current methods of identifying HTC beans are time-consuming, destructive, and unreliable. A rapid non-destructive inspection technique for pre-screening beans could help identify and discard HTC beans prior to processing. To this end, the potential of hyperspectral imaging technique covering the entire visible to near infrared (NIR) spectral range (400‒2500 nm) was evaluated for rapid and non-destructive identification of HTC beans. The HTC phenomenon was artificially induced in healthy white beans using two different combinations of suboptimal storage conditions of temperature and relative humidity (35℃, 75% RH for 45 days and 60℃, 75% RH for 10 days). Subsequently, the beans were cooked for specified durations and their hardness measured using a texture analyzer. The HTC and control (i.e. easy-to-cook (ETC)) beans were scanned with push-broom hyperspectral imaging systems. Results indicate that both sets of storage conditions rendered the beans HTC but the phenomenon induced by the two different methods was detected in different spectral ranges using hyperspectral imaging. Wavelengths across the entire visible and NIR ranges of electromagnetic spectrum were found useful in detecting HTC as beans stored at 35℃ and 75% RH for 45 days were identified mainly in the 1000‒2500 nm range and those stored at 60℃ and 75% RH for 10 days were identified in the 400‒1000 nm region. The degree of HTC defect could not be ascertained using this technique and requires further investigation.

Digital imaging and quantitative image analysis of polymer blends

1996 ◽  
Vol 54 ◽  
pp. 170-171
Author(s):  
Xiao Zhang
Keyword(s):  
Digital Imaging ◽  
Imaging Techniques ◽  
Imaging Systems ◽  
Polymer Science ◽  
Key Factors ◽  
Multiple Imaging ◽  
Quantitative Image ◽  
Digital Imaging Systems ◽  
Multi Phase ◽  
Network Technologies

Polymer microscopy involves multiple imaging techniques. Speed, simplicity, and productivity are key factors in running an industrial polymer microscopy lab. In polymer science, the morphology of a multi-phase blend is often the link between process and properties. The extent to which the researcher can quantify the morphology determines the strength of the link. To aid the polymer microscopist in these tasks, digital imaging systems are becoming more prevalent. Advances in computers, digital imaging hardware and software, and network technologies have made it possible to implement digital imaging systems in industrial microscopy labs.

scholarly journals Rapid Foreign Object Detection System on Seaweed Using VNIR Hyperspectral Imaging

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5279
Author(s):  
Dong-Hoon Kwak ◽  
Guk-Jin Son ◽  
Mi-Kyung Park ◽  
Young-Duk Kim
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Hyperspectral Imaging ◽  
High Speed ◽  
Detection System ◽  
Imaging Techniques ◽  
Foreign Object ◽  
Subtraction Method ◽  
Foreign Objects ◽  
Time Operation

The consumption of seaweed is increasing year by year worldwide. Therefore, the foreign object inspection of seaweed is becoming increasingly important. Seaweed is mixed with various materials such as laver and sargassum fusiforme. So it has various colors even in the same seaweed. In addition, the surface is uneven and greasy, causing diffuse reflections frequently. For these reasons, it is difficult to detect foreign objects in seaweed, so the accuracy of conventional foreign object detectors used in real manufacturing sites is less than 80%. Supporting real-time inspection should also be considered when inspecting foreign objects. Since seaweed requires mass production, rapid inspection is essential. However, hyperspectral imaging techniques are generally not suitable for high-speed inspection. In this study, we overcome this limitation by using dimensionality reduction and using simplified operations. For accuracy improvement, the proposed algorithm is carried out in 2 stages. Firstly, the subtraction method is used to clearly distinguish seaweed and conveyor belts, and also detect some relatively easy to detect foreign objects. Secondly, a standardization inspection is performed based on the result of the subtraction method. During this process, the proposed scheme adopts simplified and burdenless calculations such as subtraction, division, and one-by-one matching, which achieves both accuracy and low latency performance. In the experiment to evaluate the performance, 60 normal seaweeds and 60 seaweeds containing foreign objects were used, and the accuracy of the proposed algorithm is 95%. Finally, by implementing the proposed algorithm as a foreign object detection platform, it was confirmed that real-time operation in rapid inspection was possible, and the possibility of deployment in real manufacturing sites was confirmed.

scholarly journals Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3827
Author(s):  
Gemma Urbanos ◽  
Alberto Martín ◽  
Guillermo Vázquez ◽  
Marta Villanueva ◽  
Manuel Villa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Vessel ◽  
Hyperspectral Imaging ◽  
Imaging Techniques ◽  
Venous Blood ◽  
Healthy Tissue ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Arterial Blood

Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.

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