Multispectral optical imaging combinedin situwith XPS or ToFSIMS and principal component analysis (original) (raw)
Multispectral and Hyperspectral Imaging
The Encyclopedia of Archaeological Sciences, 2018
Since the invention of photography, a variety of approaches have made noninvasive imaging for cultural heritage applications possible. Multi‐ and hyperspectral imaging (together denoted spectral imaging) are two techniques that evolved from conventional color photography, having overcome its spectral limitations. Instead of imaging in three broad spectral bands, multispectral imaging acquires data in up to ten more or less equally wide and nonoverlapping spectral bands. Hyperspectral imaging goes beyond the multispectral approach by generating images in tens to hundreds of narrow, contiguous (i.e., adjacent but not overlapping) spectral bands. Most spectral imaging techniques are limited to the optical electromagnetic spectrum and acquire the reflected portion of the radiation that is used to illuminate the scene. However, imaging the emitted thermal radiation or active techniques based on laser scanners do exist as well. In cultural heritage, all these techniques are used to gain a better, noninvasive insight into the chemical and physical properties of the object(s) under investigation.
CHARISMA, 2013
In recent times, multispectral imaging techniques have increasingly become a part of the range of examination and analytical methodologies that conservation professionals have at their disposal for the investigation of cultural heritage objects. These techniques, which include both luminescence (emitted light) imaging methods (ultraviolet-induced luminescence (UVL); visible-induced infrared luminescence (VIL) and visible-induced visible luminescence (VIVL)) and a range of related broadband reflectance imaging methods (visible reflectance (VIS), infrared reflectance (IRR) and ultraviolet reflectance (UVR)), are not only used by scientists but are also increasingly being adopted by a much wider range of users including conservators, archaeologists and curators, in more diverse and challenging settings. However, although attractive in offering qualitative, non-invasive and often relatively inexpensive and portable tools for spatial localisation of specific materials or material types, the equipment, capture and processing of imagesparticularly those used in luminescence imaginghave tended to be highly dependent on individual users and the set-up they employ, making cross-comparison between different institutions and researchers very difficult. It has thus become evident that there is a need to establish a clear set of widelyaccessible methods and protocols from which to work.
Multispectral Imaging Using Multiplexed Illumination
2007 IEEE 11th International Conference on Computer Vision, 2007
Many vision tasks such as scene segmentation, or the recognition of materials within a scene, become considerably easier when it is possible to measure the spectral reflectance of scene surfaces. In this paper, we present an efficient and robust approach for recovering spectral reflectance in a scene that combines the advantages of using multiple spectral sources and a multispectral camera. We have implemented a system based on this approach using a cluster of light sources with different spectra to illuminate the scene and a conventional RGB camera to acquire images. Rather than sequentially activating the sources, we have developed a novel technique to determine the optimal multiplexing sequence of spectral sources so as to minimize the number of acquired images. We use our recovered spectral measurements to recover the continuous spectral reflectance for each scene point by using a linear model for spectral reflectance. Our imaging system can produce multispectral videos of scenes at 30fps. We demonstrate the effectiveness of our system through extensive evaluation. As a demonstration, we present the results of applying data recovered by our system to material segmentation and spectral relighting.
Principles, Instrumentation, and Applications of Infrared Multispectral Imaging, An Overview
Analytical Letters, 2005
An infrared (IR) multispectral imaging spectrometer is an instrument that can simultaneously record infrared spectroscopic and spatial information of a sample. Chemical and physical properties of the sample can be elucidated from such images. In a multispectral imaging instrument, a camera is used to record the spatial distribution of the sample, and the spectral information is obtained with a dispersive device. This overview article describes operational principles and recent development of various components used in IR multispectral imaging instruments, including the electronic dispersive devices (acousto-optic tunable filter and liquid crystal tunable filter) and near-and middle-IR cameras (InGaAs, InSb, HgCdTe, and QWIP cameras). Recent applications and unique use of the IR imaging instruments will be described followed by discussion of the future prospects of the technique.
Multispectral imaging for computer vision
2018
The main objective of this report is to provide an overview on my research activities on multispectral imaging based on spectral lter arrays. Based on this experience, we formulate future directions and challenges.
Spectrogenic imaging: A novel approach to multispectral imaging in an uncontrolled environment
Increasing the number of imaging channels beyond the conventional three has been shown to be beneficial for a wide range of applications. However, it is mostly limited to imaging in a controlled environment, where the capture environment (illuminant) is known a priori. We propose here a novel system and methodology for multispectral imaging in an uncontrolled environment. Two images of a scene, a normal RGB and a filtered RGB are captured. The illuminant under which an image is captured is estimated using a chromagenic based algorithm, and the multispectral system is calibrated automatically using the estimated illuminant. A 6-band multispectral image of a scene is obtained from the two RGB images. The spectral reflectances of the scene are then estimated using an appropriate spectral estimation method. The proposed concept and methodology is generic one, as it is valid in whatever way we acquire the two images of a scene. A system that can acquire two images of a scene can be realized, for instance in two shots using a digital camera and a filter, or in a single shot using a stereo camera, or a custom color filter array design. Simulation experiments using a stereo camera based system confirms the effectiveness of the proposed method. This could be useful in many imaging applications and computer vision.
A variety of data analysis methods can be used to enhance the information obtained from a measurement, or to simplify extraction of significant components from large data sets. Much work is needed to improve the quantification and interpretation of XPS spectra and images from complex organics. Multivariate analysis (MVA) is increasingly used for applications in electron spectroscopy to aid the analyst in interpreting the vast amount of information yielded by spectroscopic techniques. In general, the goals of MVA are to determine the number of components present, identify the chemical components, and quantify component concentrations in the mixture. Principal component analysis (PCA) is frequently used to determine the number of mathematical components which describe the data set. These mathematical components must then be related to chemically meaningful components. Various approaches to solve rotational ambiguities of spectral resolution, including local rank method (EFA), pure variables method (Simplisma) and multivariate curve resolution (MCR), are tested in the determination of chemical components from XPS data. Limitations associated with the resolution of a single matrix are shown to be partially or completely overcome when several related matrices are treated together. The test data sets contain XPS images or spectra acquired from blends of poly(vinyl chloride), PVC, and poly(methyl methacrylate), PMMA. The PVC degrades rapidly upon exposure to the X-ray beam. Spectra and images from the blend, acquired as a function of time, provide the multi-dimensional data sets for algorithm evaluation. In addition to spectral resolution, multivariate image analysis methods, such as principal component analysis, are used to extract maps of the pure components from an images-to-spectra data set.
Multispectral camera as spatio-spectrophotometer under uncontrolled illumination
Optics Express, 2019
Multispectral constancy enables the illuminant invariant representation of multispectral data. This article proposes an experimental investigation of multispectral constancy through the use of multispectral camera as a spectrophotometer for the reconstruction of surface reflectance. Three images with varying illuminations are captured and the spectra of material surfaces is reconstructed. The acquired images are transformed into canonical representation through the use of diagonal transform and spectral adaptation transform. Experimental results show that use of multispectral constancy is beneficial for both filter-wheel and snapshot multispectral cameras. The proposed concept is robust to errors in illuminant estimation and is able to perform well with linear spectral reconstruction method. This work makes us one step closer to the use of multispectral imaging for computer vision.