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Special Issue "Color & Spectral Sensors"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 September 2020).

Special Issue Editors

Prof. Dr. Javier Hernández-Andrés
Website
Guest Editor
Department of Optics, University of Granada, 18071 Granada, Spain
Interests: vision; color vision; color vision deficiencies; multispectral imaging; hyperspectral imaging; computation color imaging; spectral imaging; HDR imaging; color and spectral image processing; atmospheric optics; teaching optics
Special Issues and Collections in MDPI journals
Prof. Dr. Eva M. Valero Benito
Website
Guest Editor
Department of Optics, University of Granada, Granada, Spain
Interests: multispectral and hyperspectral imaging; color image processing; saliency prediction; spectral imaging of artwork
Special Issues and Collections in MDPI journals
Dr. Miguel A. Martínez-Domingo
Website
Guest Editor
Department of Optics, University of Granada, Granada, Spain
Interests: multispectral and hyperspectral image capture; processing and analysis; high dynamic range imaging
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Image sensors, which are among the most important components inside digital imaging systems, convert the incoming light into an electrical signal that can be viewed, analyzed, or stored. Thanks to color sensors based on primary color channels (red, green, and blue), color imaging has been widely applied in general digital imaging. When an imaging device is able to capture between three and twelve channels or spectral bands, it is usually considered a multispectral imager. If the number of spectral bands is relatively high, the device can then be considered a hyperspectral imager. Technological advances in image sensors and spectral filtering (i.e., plasmons, coded aperture, scanning sensors, multilayer sensors, etc.) have allowed the proliferation of color, multispectral, and hyperspectral imaging systems for image capture in a wide range of fields, such as medicine, remote sensing, biology, cosmetics, quality control, surveillance, food industry, art observation, cultural heritage, and art, just to name a few. The present Special Issue on “Color and spectral sensors” aims to present recent advances in new optical sensor technologies and in the development of new techniques for processing color and/or spectral information and to demonstrate their potential for different applications, according, but not limited to, the list of keywords below. In addition to original research papers with novel findings, review articles describing the current state of the art and future perspectives are invited.

Prof. Dr. Javier Hernández-Andrés
Prof. Dr. Eva M. Valero Benito
Dr. Miguel A. Martínez-Domingo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • recent optical sensor technologies: plasmonic-based devices, coded-aperture systems, snapshot, scanning sensors, multilayer sensors, sparse sensors, HDR, and others.
  • application domains: spectral reconstruction, object recognition, underwater, biomedical applications, aids for the visually impaired, depth and stereo, color constancy, cultural heritage and art, HDR, robotic vision, food analysis, agriculture, waste sorting, and others.
  • demosaicing algorithms for color/spectral imaging
  • imaging sensors calibration
  • band optimization algorithms for spectral imaging
  • multiband fusion/blending
  • deep-learning applied to spectral image analysis and optimization of imaging systems

Published Papers (18 papers)

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Open AccessArticle
A Mathematical Investigation into the Design of Prefilters That Make Cameras More Colorimetric
Sensors 2020, 20(23), 6882; https://doi.org/10.3390/s20236882 - 02 Dec 2020
Abstract
By placing a color filter in front of a camera we make new spectral sensitivities. The Luther-condition optimization solves for a color filter so that the camera’s filtered sensitivities are as close to being linearly related to the XYZ color matching functions (CMFs) [...] Read more.
By placing a color filter in front of a camera we make new spectral sensitivities. The Luther-condition optimization solves for a color filter so that the camera’s filtered sensitivities are as close to being linearly related to the XYZ color matching functions (CMFs) as possible, that is, a filter is found that makes the camera more colorimetric. Arguably, the more general Vora-Value approach solves for the filter that best matches all possible target spectral sensitivity sets (e.g., any linear combination of the XYZ CMFs). A concern that we investigate here is that the filters found by the Luther and Vora-Value optimizations are different from one another. In this paper, we unify the Luther and Vora-Value approaches to prefilter design. We prove that if the target of the Luther-condition optimization is an orthonormal basis—a special linear combination of the XYZ CMFs which are orthogonal and are in unit length—the discovered Luther-filter is also the filter that maximizes the Vora-Value. A key advantage of using the Luther-condition formulation to maximize the Vora-Value is that it is both simpler to implement and converges to its optimal answer more quickly. Experiments validate our method. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
Single Image Dehazing Algorithm Analysis with Hyperspectral Images in the Visible Range
Sensors 2020, 20(22), 6690; https://doi.org/10.3390/s20226690 - 23 Nov 2020
Abstract
In foggy or hazy conditions, images are degraded due to the scattering and attenuation of atmospheric particles, reducing the contrast and visibility and changing the color. This degradation depends on the distance, the density of the atmospheric particles and the wavelength. We have [...] Read more.
In foggy or hazy conditions, images are degraded due to the scattering and attenuation of atmospheric particles, reducing the contrast and visibility and changing the color. This degradation depends on the distance, the density of the atmospheric particles and the wavelength. We have tested and applied five single image dehazing algorithms, originally developed to work on RGB images and not requiring user interaction and/or prior knowledge about the images, on a spectral hazy image database in the visible range. We have made the evaluation using two strategies: the first is based on the analysis of eleven state-of-the-art metrics and the second is two psychophysical experiments with 126 subjects. Our results suggest that the higher the wavelength within the visible range is, the higher the quality of the dehazed images. The quality increases for low haze/fog levels. The choice of the best performing algorithm depends on the criterion prioritized by the metric design strategy. The psychophysical experiment results show that the level of agreement between observers and metrics depends on the criterion set for the observers’ task. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
Physically Plausible Spectral Reconstruction
Sensors 2020, 20(21), 6399; https://doi.org/10.3390/s20216399 - 09 Nov 2020
Abstract
Spectral reconstruction algorithms recover spectra from RGB sensor responses. Recent methods—with the very best algorithms using deep learning—can already solve this problem with good spectral accuracy. However, the recovered spectra are physically incorrect in that they do not induce the RGBs from which [...] Read more.
Spectral reconstruction algorithms recover spectra from RGB sensor responses. Recent methods—with the very best algorithms using deep learning—can already solve this problem with good spectral accuracy. However, the recovered spectra are physically incorrect in that they do not induce the RGBs from which they are recovered. Moreover, if the exposure of the RGB image changes then the recovery performance often degrades significantly—i.e., most contemporary methods only work for a fixed exposure. In this paper, we develop a physically accurate recovery method: the spectra we recover provably induce the same RGBs. Key to our approach is the idea that the set of spectra that integrate to the same RGB can be expressed as the sum of a unique fundamental metamer (spanned by the camera’s spectral sensitivities and linearly related to the RGB) and a linear combination of a vector space of metameric blacks (orthogonal to the spectral sensitivities). Physically plausible spectral recovery resorts to finding a spectrum that adheres to the fundamental metamer plus metameric black decomposition. To further ensure spectral recovery that is robust to changes in exposure, we incorporate exposure changes in the training stage of the developed method. In experiments we evaluate how well the methods recover spectra and predict the actual RGBs and RGBs under different viewing conditions (changing illuminations and/or cameras). The results show that our method generally improves the state-of-the-art spectral recovery (with more stabilized performance when exposure varies) and provides zero colorimetric error. Moreover, our method significantly improves the color fidelity under different viewing conditions, with up to a 60% reduction in some cases. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
How Good Are RGB Cameras Retrieving Colors of Natural Scenes and Paintings?—A Study Based on Hyperspectral Imaging
Sensors 2020, 20(21), 6242; https://doi.org/10.3390/s20216242 - 01 Nov 2020
Abstract
RGB digital cameras (RGB) compress the spectral information into a trichromatic system capable of approximately representing the actual colors of objects. Although RGB digital cameras follow the same compression philosophy as the human eye (OBS), the spectral sensitivity is different. To what extent [...] Read more.
RGB digital cameras (RGB) compress the spectral information into a trichromatic system capable of approximately representing the actual colors of objects. Although RGB digital cameras follow the same compression philosophy as the human eye (OBS), the spectral sensitivity is different. To what extent they provide the same chromatic experiences is still an open question, especially with complex images. We addressed this question by comparing the actual colors derived from spectral imaging with those obtained with RGB cameras. The data from hyperspectral imaging of 50 natural scenes and 89 paintings was used to estimate the chromatic differences between OBS and RGB. The corresponding color errors were estimated and analyzed in the color spaces CIELAB (using the color difference formulas ΔE*ab and CIEDE2000), Jzazbz, and iCAM06. In CIELAB the most frequent error (using ΔE*ab) found was 5 for both paintings and natural scenes, a similarity that held for the other spaces tested. In addition, the distribution of errors across the color space shows that the errors are small in the achromatic region and increase with saturation. Overall, the results indicate that the chromatic errors estimated are close to the acceptance error and therefore RGB digital cameras are able to produce quite realistic colors of complex scenarios. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
Brightness Invariant Deep Spectral Super-Resolution
Sensors 2020, 20(20), 5789; https://doi.org/10.3390/s20205789 - 13 Oct 2020
Abstract
Spectral reconstruction from RGB or spectral super-resolution (SSR) offers a cheap alternative to otherwise costly and more complex spectral imaging devices. In recent years, deep learning based methods consistently achieved the best reconstruction quality in terms of spectral error metrics. However, there are [...] Read more.
Spectral reconstruction from RGB or spectral super-resolution (SSR) offers a cheap alternative to otherwise costly and more complex spectral imaging devices. In recent years, deep learning based methods consistently achieved the best reconstruction quality in terms of spectral error metrics. However, there are important properties that are not maintained by deep neural networks. This work is primarily dedicated to scale invariance, also known as brightness invariance or exposure invariance. When RGB signals only differ in their absolute scale, they should lead to identical spectral reconstructions apart from the scaling factor. Scale invariance is an essential property that signal processing must guarantee for a wide range of practical applications. At the moment, scale invariance can only be achieved by relying on a diverse database during network training that covers all possibly occurring signal intensities. In contrast, we propose and evaluate a fundamental approach for deep learning based SSR that holds the property of scale invariance by design and is independent of the training data. The approach is independent of concrete network architectures and instead focuses on reevaluating what neural networks should actually predict. The key insight is that signal magnitudes are irrelevant for acquiring spectral reconstructions from camera signals and are only useful for a potential signal denoising. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
Spectral Color Management in Virtual Reality Scenes
Sensors 2020, 20(19), 5658; https://doi.org/10.3390/s20195658 - 03 Oct 2020
Abstract
Virtual reality has reached a great maturity in recent years. However, the quality of its visual appearance still leaves room for improvement. One of the most difficult features to represent in real-time 3D rendered virtual scenes is color fidelity, since there are many [...] Read more.
Virtual reality has reached a great maturity in recent years. However, the quality of its visual appearance still leaves room for improvement. One of the most difficult features to represent in real-time 3D rendered virtual scenes is color fidelity, since there are many factors influencing the faithful reproduction of color. In this paper we introduce a method for improving color fidelity in virtual reality systems based in real-time 3D rendering systems. We developed a color management system for 3D rendered scenes divided into two levels. At the first level, color management is applied only to light sources defined inside the virtual scene. At the second level, we applied spectral techniques over the hyperspectral textures of 3D objects to obtain a higher degree of color fidelity. To illustrate the application of this color management method, we simulated a virtual version of the Ishihara test for color blindness deficiency detection. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
Surgical Guidance for Removal of Cholesteatoma Using a Multispectral 3D-Endoscope
Sensors 2020, 20(18), 5334; https://doi.org/10.3390/s20185334 - 17 Sep 2020
Cited by 1
Abstract
We develop a stereo-multispectral endoscopic prototype in which a filter-wheel is used for surgical guidance to remove cholesteatoma tissue in the middle ear. Cholesteatoma is a destructive proliferating tissue. The only treatment for this disease is surgery. Removal is a very demanding task, [...] Read more.
We develop a stereo-multispectral endoscopic prototype in which a filter-wheel is used for surgical guidance to remove cholesteatoma tissue in the middle ear. Cholesteatoma is a destructive proliferating tissue. The only treatment for this disease is surgery. Removal is a very demanding task, even for experienced surgeons. It is very difficult to distinguish between bone and cholesteatoma. In addition, it can even reoccur if not all tissue particles of the cholesteatoma are removed, which leads to undesirable follow-up operations. Therefore, we propose an image-based method that combines multispectral tissue classification and 3D reconstruction to identify all parts of the removed tissue and determine their metric dimensions intraoperatively. The designed multispectral filter-wheel 3D-endoscope prototype can switch between narrow-band spectral and broad-band white illumination, which is technically evaluated in terms of optical system properties. Further, it is tested and evaluated on three patients. The wavelengths 400 nm and 420 nm are identified as most suitable for the differentiation task. The stereoscopic image acquisition allows accurate 3D surface reconstruction of the enhanced image information. The first results are promising, as the cholesteatoma can be easily highlighted, correctly identified, and visualized as a true-to-scale 3D model showing the patient-specific anatomy. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
Spectral Reflectance Reconstruction Using Fuzzy Logic System Training: Color Science Application
Sensors 2020, 20(17), 4726; https://doi.org/10.3390/s20174726 - 21 Aug 2020
Abstract
In this work, we address the problem of spectral reflectance recovery from both CIEXYZ and RGB values by means of a machine learning approach within the fuzzy logic framework, which constitutes the first application of fuzzy logic in these tasks. We train a [...] Read more.
In this work, we address the problem of spectral reflectance recovery from both CIEXYZ and RGB values by means of a machine learning approach within the fuzzy logic framework, which constitutes the first application of fuzzy logic in these tasks. We train a fuzzy logic inference system using the Macbeth ColorChecker DC and we test its performance with a 130 sample target set made out of Artist’s paints. As a result, we obtain a fuzzy logic inference system (FIS) that performs quite accurately. We have studied different parameter settings within the training to achieve a meaningful overfitting-free system. We compare the system performance against previous successful methods and we observe that both spectrally and colorimetrically our approach substantially outperforms these classical methods. In addition, from the FIS trained we extract the fuzzy rules that the system has learned, which provide insightful information about how the RGB/XYZ inputs are related to the outputs. That is to say that, once the system is trained, we extract the codified knowledge used to relate inputs and outputs. Thus, we are able to assign a physical and/or conceptual meaning to its performance that allows not only to understand the procedure applied by the system but also to acquire insight that in turn might lead to further improvements. In particular, we find that both trained systems use four reference spectral curves, with some similarities, that are combined in a non-linear way to predict spectral curves for other inputs. Notice that the possibility of being able to understand the method applied in the trained system is an interesting difference with respect to other ’black box’ machine learning approaches such as the currently fashionable convolutional neural networks in which the downside is the impossibility to understand their ways of procedure. Another contribution of this work is to serve as an example of how, through the construction of a FIS, some knowledge relating inputs and outputs in ground truth datasets can be extracted so that an analogous strategy could be followed for other problems in color and spectral science. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
The Geometry of Noise in Color and Spectral Image Sensors
Sensors 2020, 20(16), 4487; https://doi.org/10.3390/s20164487 - 11 Aug 2020
Abstract
Digital images are always affected by noise and the reduction of its impact is an active field of research. Noise due to random photon fall onto the sensor is unavoidable but could be amplified by the camera image processing such as in the [...] Read more.
Digital images are always affected by noise and the reduction of its impact is an active field of research. Noise due to random photon fall onto the sensor is unavoidable but could be amplified by the camera image processing such as in the color correction step. Color correction is expressed as the combination of a spectral estimation and a computation of color coordinates in a display color space. Then we use geometry to depict raw, spectral and color signals and noise. Geometry is calibrated on the physics of image acquisition and spectral characteristics of the sensor to study the impact of the sensor space metric on noise amplification. Since spectral channels are non-orthogonal, we introduce the contravariant signal to noise ratio for noise evaluation at spectral reconstruction level. Having definitions of signal to noise ratio for each steps of spectral or color reconstruction, we compare performances of different types of sensors (RGB, RGBW, RGBWir, CMY, RYB, RGBC). Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
A Novel Approach to Using Spectral Imaging to Classify Dyes in Colored Fibers
Sensors 2020, 20(16), 4379; https://doi.org/10.3390/s20164379 - 05 Aug 2020
Abstract
In the field of cultural heritage, applied dyes on textiles are studied to explore their great artistic and historic values. Dye analysis is essential and important to plan correct restoration, preservation and display strategy in museums and art galleries. However, most of the [...] Read more.
In the field of cultural heritage, applied dyes on textiles are studied to explore their great artistic and historic values. Dye analysis is essential and important to plan correct restoration, preservation and display strategy in museums and art galleries. However, most of the existing diagnostic technologies are destructive to the historical objects. In contrast to that, spectral reflectance imaging is potential as a non-destructive and spatially resolved technique. There have been hardly any studies in classification of dyes in textile fibers using spectral imaging. In this study, we show that spectral imaging with machine learning technique is capable in preliminary screening of dyes into the natural or synthetic class. At first, sparse logistic regression algorithm is applied on reflectance data of dyed fibers to determine some discriminating bands. Then support vector machine algorithm (SVM) is applied for classification considering the reflectance of the selected spectral bands. The results show nine selected bands in short wave infrared region (SWIR, 1000–2500 nm) classify dyes with 97.4% accuracy (kappa 0.94). Interestingly, the results show that fairly accurate dye classification can be achieved using the bands at 1480nm, 1640 nm, and 2330 nm. This indicates possibilities to build an inexpensive handheld screening device for field studies. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
Spatial–Spectral Evidence of Glare Influence on Hyperspectral Acquisitions
Sensors 2020, 20(16), 4374; https://doi.org/10.3390/s20164374 - 05 Aug 2020
Cited by 1
Abstract
Glare is an unwanted optical phenomenon which affects imaging systems with optics. This paper presents for the first time a set of hyperspectral image (HSI) acquisitions and measurements to verify how glare affects acquired HSI data in standard conditions. We acquired two ColorCheckers [...] Read more.
Glare is an unwanted optical phenomenon which affects imaging systems with optics. This paper presents for the first time a set of hyperspectral image (HSI) acquisitions and measurements to verify how glare affects acquired HSI data in standard conditions. We acquired two ColorCheckers (CCs) in three different lighting conditions, with different backgrounds, different exposure times, and different orientations. The reflectance spectra obtained from the imaging system have been compared to pointwise reference measures obtained with contact spectrophotometers. To assess and identify the influence of glare, we present the Glare Effect (GE) index, which compares the contrast of the grayscale patches of the CC in the hyperspectral images with the contrast of the reference spectra of the same patches. We evaluate, in both spatial and spectral domains, the amount of glare affecting every hyperspectral image in each acquisition scenario, clearly evidencing an unwanted light contribution to the reflectance spectra of each point, which increases especially for darker pixels and pixels close to light sources or bright patches. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
Art through the Colors of Graffiti: From the Perspective of the Chromatic Structure
Sensors 2020, 20(9), 2531; https://doi.org/10.3390/s20092531 - 29 Apr 2020
Abstract
Graffiti is a general term that describes inscriptions on a wall, a practice with ancient origins, ranging from simple drawings and writings to elaborate pictorial representations. Nowadays, the term graffiti commonly describes the street art dedicated to wall paintings, which raises complex questions, [...] Read more.
Graffiti is a general term that describes inscriptions on a wall, a practice with ancient origins, ranging from simple drawings and writings to elaborate pictorial representations. Nowadays, the term graffiti commonly describes the street art dedicated to wall paintings, which raises complex questions, including sociological, legal, political and aesthetic issues. Here we examine the aesthetics of graffiti colors by quantitatively characterizing and comparing their chromatic structure to that of traditional paintings in museums and natural scenes obtained by hyperspectral imaging. Two hundred twenty-eight photos of graffiti were taken in the city of São Paulo, Brazil. The colors of graffiti were represented in a color space and characterized by several statistical parameters. We found that graffiti have chromatic structures similar to those of traditional paintings, namely their preferred colors, distribution, and balance. In particular, they have color gamuts with the same degree of elongation, revealing a tendency for combining similar colors in the same proportions. Like more traditional artists, the preferred colors are close to the yellow–blue axis of color space, suggesting that graffiti artists’ color choices also mimic those of the natural world. Even so, graffiti tend to have larger color gamuts due to the availability of a new generation of synthetic pigments, resulting in a greater freedom in color choice. A complementary analysis of graffiti from other countries supports the global generalization of these findings. By sharing their color structures with those of paintings, graffiti contribute to bringing art to the cities. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
Spectral Filter Selection for Increasing Chromatic Diversity in CVD Subjects
Sensors 2020, 20(7), 2023; https://doi.org/10.3390/s20072023 - 03 Apr 2020
Cited by 2
Abstract
This paper analyzes, through computational simulations, which spectral filters increase the number of discernible colors (NODC) of subjects with normal color vision, as well as red–green anomalous trichromats and dichromats. The filters are selected from a set of filters in which we have [...] Read more.
This paper analyzes, through computational simulations, which spectral filters increase the number of discernible colors (NODC) of subjects with normal color vision, as well as red–green anomalous trichromats and dichromats. The filters are selected from a set of filters in which we have modeled spectral transmittances. With the selected filters we have carried out simulations performed using the spectral reflectances captured either by a hyperspectral camera or by a spectrometer. We have also studied the effects of these filters on color coordinates. Finally, we have simulated the results of two widely used color blindness tests: Ishihara and Farnsworth–Munsell 100 Hue (FM100). In these analyses the selected filters are compared with the commercial filters from EnChroma and VINO companies. The results show that the increase in NODC with the selected filters is not relevant. The simulation results show that none of these chosen filters help color vision deficiency (CVD) subjects to pass the set of color blindness tests studied. These results obtained using standard colorimetry support the hypothesis that the use of color filters does not cause CVDs to have a perception similar to that of a normal observer. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
RGB-D Image Processing Algorithm for Target Recognition and Pose Estimation of Visual Servo System
Sensors 2020, 20(2), 430; https://doi.org/10.3390/s20020430 - 12 Jan 2020
Cited by 3
Abstract
This paper studies the control performance of visual servoing system under the planar camera and RGB-D cameras, the contribution of this paper is through rapid identification of target RGB-D images and precise measurement of depth direction to strengthen the performance indicators of visual [...] Read more.
This paper studies the control performance of visual servoing system under the planar camera and RGB-D cameras, the contribution of this paper is through rapid identification of target RGB-D images and precise measurement of depth direction to strengthen the performance indicators of visual servoing system such as real time and accuracy, etc. Firstly, color images acquired by the RGB-D camera are segmented based on optimized normalized cuts. Next, the gray scale is restored according to the histogram feature of the target image. Then, the obtained 2D graphics depth information and the enhanced gray image information are distort merged to complete the target pose estimation based on the Hausdorff distance, and the current image pose is matched with the target image pose. The end angle and the speed of the robot are calculated to complete a control cycle and the process is iterated until the servo task is completed. Finally, the performance index of this control system based on proposed algorithm is tested about accuracy, real-time under position-based visual servoing system. The results demonstrate and validate that the RGB-D image processing algorithm proposed in this paper has the performance in the above aspects of the visual servoing system. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
Spectral Image Processing for Museum Lighting Using CIE LED Illuminants
Sensors 2019, 19(24), 5400; https://doi.org/10.3390/s19245400 - 07 Dec 2019
Cited by 6
Abstract
This work presents a spectral color-imaging procedure for the detailed colorimetric study of real artworks under arbitrary illuminants. The results demonstrate this approach to be a powerful tool for art and heritage professionals when deciding which illumination to use in museums, or which [...] Read more.
This work presents a spectral color-imaging procedure for the detailed colorimetric study of real artworks under arbitrary illuminants. The results demonstrate this approach to be a powerful tool for art and heritage professionals when deciding which illumination to use in museums, or which conservation or restoration techniques best maintain the color appearance of the original piece under any illuminant. Spectral imaging technology overcomes the limitations of common area-based point-measurement devices such as spectrophotometers, allowing a local study either pixelwise or by selected areas. To our knowledge, this is the first study available that uses the proposed CIE (Commission Internationale de l’Éclairage) light-emitting diode (LED) illuminants in the context of art and heritage science, comparing them with the three main CIE illuminants A, D50, and D65. For this, the corresponding colors under D65 have been calculated using a chromatic adaptation transform analogous to the one in CIECAM02. For the sample studied, the CIE LED illuminants with the lowest average CIEDE2000 color differences from the standard CIE illuminants are LED-V1 for A and LED-V2 for D50 and D65, with 1.23, 1.07, and 1.57 units, respectively. The work studied is a Moorish epigraphic frieze of plasterwork with a tiled skirting from the Nasrid period (12th–15th centuries) exhibited in the Museum of the Alhambra (Granada, Spain). Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessArticle
Ultrathin Submicrometer Scale Multicolor Detector of Visible Light Based on Metamaterial
Sensors 2019, 19(19), 4103; https://doi.org/10.3390/s19194103 - 23 Sep 2019
Abstract
In this study, we propose a multi-color detector using a simple plasmonic metamaterial structure consisting of a silver and a indium phosphide. The color detector is composed of a metal strip with a periodicity in the x-axis direction on a layer of [...] Read more.
In this study, we propose a multi-color detector using a simple plasmonic metamaterial structure consisting of a silver and a indium phosphide. The color detector is composed of a metal strip with a periodicity in the x-axis direction on a layer of the dielectric material located on a metal substrate. This color detector can control the spectrum absorbed in the dielectric material layer by changing the thickness of the dielectric material layer or the width of the metal strip. The triangle formed by the three primary colors, namely, red, green, and blue, which are representatively detected by optimizing the color detector using only silver and indium phosphide, covers 44% of the standard Red Green Blue (sRGB) region. Furthermore, the area of the triangle obtained by further optimization, such as changing the material to gold or gallium phosphide or changing the period of the metal stirp, can aid in the detection of a larger number of colors covering 108% of the sRGB area. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessLetter
Color Sensor Accuracy Index Utilizing Metamer Mismatch Radii
Sensors 2020, 20(15), 4275; https://doi.org/10.3390/s20154275 - 31 Jul 2020
Cited by 1
Abstract
A novel method is described for evaluating the colorimetric accuracy of digital color cameras based on a new measure of the metamer mismatch body (MMB) that is induced by the change from the camera as an ‘observer’ to the human standard observer. In [...] Read more.
A novel method is described for evaluating the colorimetric accuracy of digital color cameras based on a new measure of the metamer mismatch body (MMB) that is induced by the change from the camera as an ‘observer’ to the human standard observer. In comparison to the majority of existing methods for evaluating colorimetric accuracy, the advantage of using the MMB is that it is based on the theory of metamer mismatching and, therefore, shows how much color error can arise in principle. A new measure of colorimetric accuracy based on the shape of the camera-induced MMB is proposed and tested. MMB shape is measured in terms of the moments of inertia of the MMB treated as a mass of uniform density. Since colorimetric accuracy is independent of any linear transformation of the sensor space, the MMB measure needs to be as well. Normalization by the moments of inertia of the object color solid is introduced to provide this independence. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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Open AccessLetter
Spectroscopic Evaluation of Red Blood Cells of Thalassemia Patients with Confocal Microscopy: A Pilot Study
Sensors 2020, 20(14), 4039; https://doi.org/10.3390/s20144039 - 21 Jul 2020
Abstract
Hemoglobinopathies represent the most common single-gene defects in the world and pose a major public health problem, particularly in tropical countries, where they occur with high frequency. Diagnosing hemoglobinopathies can sometimes be difficult due to the coexistence of different causes of anemia, such [...] Read more.
Hemoglobinopathies represent the most common single-gene defects in the world and pose a major public health problem, particularly in tropical countries, where they occur with high frequency. Diagnosing hemoglobinopathies can sometimes be difficult due to the coexistence of different causes of anemia, such as thalassemia and iron deficiency, and blood transfusions, among other factors, and requires expensive and complex molecular tests. This work explores the possibility of using spectral confocal microscopy as a diagnostic tool for thalassemia in pediatric patients, a disease caused by mutations in the globin genes that result in changes of the globin chains that form hemoglobin—in pediatric patients. Red blood cells (RBCs) from patients with different syndromes of alpha-thalassemia and iron deficiency (including anemia) as well as healthy (control) subjects were analyzed under a Leica TCS SP8 confocal microscope following different image acquisition protocols. We found that diseased RBCs exhibited autofluorescence when excited at 405 nm and their emission was collected in the spectral range from 425 nm to 790 nm. Three experimental descriptors calculated from the mean emission intensities at 502 nm, 579 nm, 628 nm, and 649 nm allowed us to discriminate between diseased and healthy cells. According to the results obtained, spectral confocal microscopy could serve as a tool in the diagnosis of thalassemia. Full article
(This article belongs to the Special Issue Color & Spectral Sensors)
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