Single Color Research Articles

Measurement of Photosynthetic Pigment Content using Convolutional Neural Network

Estimation of photosynthetic pigment levels from leaves can be done using conventional methods using laboratory equipment such as spectrophotometers and using digital image processing from leaf images with a computational model. In digital image processing methods, various models are used, such as neural network, CNN, and linear regression. Measurement of photosynthetic pigment levels using image processing methods uses color value data from image data as input to the model used. In this study, we will analyze the effect of various types of color space and inpaint preprocessing settings on the accuracy of the CNN model in measuring leaf photosynthetic pigment levels. The color space types being tested are 4 single color spaces RGB, HSV, LAB, and YCbCr, as well as 6 color combination spaces RGB+HSV, RGB+LAB, RGB+YCbCr, HSV+LAB, HSV+YCbCr, and LAB+YCbCr. The choice of the type of color space takes into account the phenomenon of color constancy and the characteristics of the color space on the lighting elements. In addition, image data is divided into two types, namely through inpaint preprocessing and not, so that in total there are 20 types of input data. After the CNN model training process with various types of color spaces and different preprocessing settings as input data, observations were made on the accuracy values, namely the training MAE and the validation MAE for each model. From 20 types of input data, 3 types of input data are obtained which are recommended as input data that provide the best model accuracy value based on MAE validation with values ​​of 0.08761, 0.09252, and 0.09288. The three recommended input data from the sequence of accuracy values ​​are RGB+LAB without inpaint, RGB with inpaint, and LAB+YCbCr without inpaint.

Production technology for Roman mosaic glass vessels: new data from the domus of Tito Macro in Aquileia (Italy)

• Focus on Roman mosaic glass vessels, dated to the 1st cent. AD, from Aquileia (Italy). • Base glass, colourants and opacifiers consistent with Roman glass-making tradition. • No links between mosaic glass vessels and specific primary furnaces. • Mosaic vessels-makers acquired coloured rods and canes from different suppliers. • Production model of mosaic glass vessels seems to be composed of at least three steps. The present work focuses on five mosaic glass vessels from the domus of Tito Macro in Aquileia (northeastern Italy). The vessels were typogically attributed to three kinds of mosaic glass (marbled, striped mosaic, and composite mosaic patterns), all dated to the 1st century AD and obtained by the juxtaposition of translucent/transparent and opaque coloured glass pieces for a total of 25 analytical samples., They were texturally and chemically analysed by optical microscopy (OM), scanning electron microscopy coupled with energy-dispersive spectrometry (SEM-EDS), and electron microprobe (EPMA). The results show that base glass, colourants and opacifiers are consistent with the early Roman glass-making tradition. Textural and chemical data suggest that the vessel makers acquired coloured rods and canes from different suppliers. There were no links between vessels and specific primary furnaces or amongst various colours of different vessels and similar colours in the same object, suggesting a complex production organisation, especially for composite canes, where the production of the single colour rods was probably separated from that of the canes. The effects of working processes on glass composition are also investigated.

Optical and Spin Properties of NV Center Ensembles in Diamond Nano-Pillars.

Nitrogen-vacancy (NV) color centers in diamond are excellent quantum sensors possessing high sensitivity and nano-scale spatial resolution. Their integration in photonic structures is often desired, since it leads to an increased photon emission and also allows the realization of solid-state quantum technology architectures. Here, we report the fabrication of diamond nano-pillars with diameters up to 1000 nm by electron beam lithography and inductively coupled plasma reactive ion etching in nitrogen-rich diamonds (type Ib) with [100] and [111] crystal orientations. The NV centers were created by keV-He ion bombardment and subsequent annealing, and we estimate an average number of NVs per pillar to be 4300 ± 300 and 520 ± 120 for the [100] and [111] samples, respectively. Lifetime measurements of the NVs’ excited state showed two time constants with average values of τ1 ≈ 2 ns and τ2 ≈ 8 ns, which are shorter as compared to a single color center in a bulk crystal (τ ≈ 10 ns). This is probably due to a coupling between the NVs as well as due to interaction with bombardment-induced defects and substitutional nitrogen (P1 centers). Optically detected magnetic resonance measurements revealed a contrast of about 5% and average coherence and relaxation times of T2 [100] = 420 ± 40 ns, T2 [111] = 560 ± 50 ns, and T1 [100] = 162 ± 11 μs, T1 [111] = 174 ± 24 μs. These pillars could find an application for scanning probe magnetic field imaging.

An Observational Study to Evaluate Single Deepest Vertical Pocket (SDP) and Color Doppler Indices (CDI) with Severity of Covid in Pregnant Women with COVID-19

An Observational Study to Evaluate Single Deepest Vertical Pocket (SDP) and Color Doppler Indices (CDI) with Severity of Covid in Pregnant Women with COVID-19

Decomposition and replacement: Spatial knowledge distillation for monocular depth estimation

Knowledge distillation has become a key technique for making smart and light-weight networks through model compression and transfer learning. Unlike previous methods that applied knowledge distillation to the classification task, we propose to exploit the decomposition-and-replacement based distillation scheme for depth estimation from a single RGB color image. To do this, Laplacian pyramid-based knowledge distillation is firstly presented in this paper. The key idea of the proposed method is to transfer the rich knowledge of the scene depth, which is well encoded through the teacher network, to the student network in a structured way by decomposing it into the global context and local details. This is fairly desirable for the student network to restore the depth layout more accurately with limited resources. Moreover, we also propose a new guidance concept for knowledge distillation, so-called ReplaceBlock, which replaces blocks randomly selected in the decoded feature of the student network with those of the teacher network. Our ReplaceBlock gives a smoothing effect in learning the feature distribution of the teacher network by considering the spatial contiguity in the feature space. This process is also helpful to clearly restore the depth layout without the significant computational cost. Based on various experimental results on benchmark datasets, the effectiveness of our distillation scheme for monocular depth estimation is demonstrated in details. The code and model are publicly available at : https://github.com/tjqansthd/Lap_Rep_KD_Depth.

Arrival time fluctuation of the SwissFEL photocathode laser: characterization by a single color balanced cross correlator.

The arrival time jitter and drift of the photocathode drive laser has an important impact on the performance of a Free-Electron-Laser (FEL). It adversely affects the beam energy jitter, bunch length jitter and bunch arrival time jitter, which becomes especially important for pump-probe experiments with femtosecond time resolution. To measure both parameters background free and stabilize the drift of the Yb:CaF2 based laser we use a well designed balanced optical cross correlator. In this paper we present our results using this device and focus particularly on the performance of the amplifier. We achieve a laser drift of less than 200 fs during 60 h, a 4.5 fs rms jitter of the amplifier relative to its seeding oscillator and 11 fs rms for the whole laser relative to a reference clock integrated from 2 mHz to 100 Hz.

Quality Assessment of Components of Wheat Seed Using Different Classifications Models

To use machine vision technology in visual quality control of cereal seeds, sufficient knowledge is necessary. In this work, the capability of machine visual systems, equipped with industrial digital cameras for the identification and classification of seven-grain groups in wheat seed samples, was studied. Two statistical models and three support vector machines were employed in this study. Through image processing of 21,000 single grains, the shape, colour, and textural features of each grain were determined. Ninety-one features were ranked through the ReliefF method. The shape features were the most prominent, followed by the textural and colour features. Among the five models tested, the highest classification accuracy was obtained using quadratic support vector machine (QSVM) and the first 35 features. In the test run of this model with independent data, the classification accuracy for sound white wheat, small white wheat, broken white wheat, shrunken white wheat, red wheat, barley and rye were, respectively, 98.7, 98, 99.3, 90.7, 99, 100, and 97.3%, with an overall average accuracy of 97.6%. In the context of this study, the machine vision system—comprising an industrial digital camera and quadratic support vector machine or non-linear discriminate analysis method—was identified as a valuable system in the investigation of the visual qualities of wheat seeds.

Building a spectral cytometry toolbox: Coupling fluorescent proteins and antibodies to microspheres.

Fluorescent proteins (FPs) have become an essential tool for biological research. Since the isolation and description of GFP, hundreds of fluorescent proteins have been discovered and created with various characteristics. The excitation of these proteins ranges from ultra-violet (UV) up to near infra-RED (NIR). Using conventional cytometry with each detector assigned to each fluorochrome, great care must be taken when selecting the optimal bandpass filters to minimalize the spectral overlap. In the last 8 years, several companies have released full spectrum flow cytometers which eliminates the need to change optical filters for analyzing FPs. This addressed at least part of the problem however, the laser wavelengths in commercial instruments are generally not ideal for all fluorescent proteins yet do allow the separation of at least six FPs. Another technical challenge is to have convenient single color controls. If four different FPs are being used in an experiment, single color controls will be needed to compensate or unmix the data. In the case of cultured cells this will involve having each of the FPs expressed in cell lines separately with a parental cell line expressing none. In the case of in vivo experiments, colonies of animals may need to be maintained expressing each FP along with a wildtype animal. This represents a considerable expense and inconvenience. An appealing alternative is to produce and purify FPs and covalently couple to polystyrene microspheres. Such microspheres are ready to use and can be stored at 4°C for months or even years without any deterioration in fluorescence. The same procedure can be used to couple antibodies to these particles. Here we describe this procedure which can be executed in any lab without any special equipment or skills.

Two dimensional quaternion valued singular spectrum analysis with application to image denoising

Compared to the traditional single color plane based image denoising methods, the quaternion valued singular value decomposition (QSVD) exploits the relationship among different color planes. Hence, it has been applied to the color image denoising. On the other hand, compared to the non-overlapping based image denoising methods, the two dimensional real valued singular spectrum analysis (2DSSA) constructs the trajectory matrix with many elements in the matrix being overlapped. Since the 2DSSA exploits the local information within each color plane, it has also been applied to the single color plane based image denoising. However, neither these two image denoising methods can exploit the relationship among the color planes and the local information within each color plane simultaneously. Therefore, this paper proposes a two dimensional quaternion valued singular spectrum analysis (2DQSSA) based method for performing the color image denoising. Our proposed method can enjoy the advantages of both methods. However, the most critical issue for the 2DQSSA is on the selection of these 2DQSSA components. This paper finds that the optimal total number of the 2DQSSA components used for performing the reconstruction is monotonic decreasing with respect to the power of the noise in the image. Therefore, the polynomial fitting approach is proposed to model this relationship. Since the 2DQSSA based denoising method exploits the relationship among the red color plane, the green color plane and the blue color plane, the 2DQSSA based denoising method outperforms the conventional single color plane based denoising methods. Moreover, since the 2DQSSA based denoising method also exploits the local relationship within each color plane, the 2DQSSA based denoising method outperforms the non-overlapping based methods.

A lightweight convolutional network for infrared object detection and tracking

As an important application of computer vision, visual tracking has being a fundamental topic. Compared with visible image, infrared image has the characteristic of low resolution, blurred contour and single color feature. Thus, it is still a challenge for infrared object tracking. Further, it is difficult to balance the real-time performance and accuracy. This paper proposed a method for target detection and tracking, with a deeper and lightweight MobileNet V2 structure as the backbone network. In the end, the tracker is tested on various datasets. Result shows that the tracker can get a balance between tracking accuracy and inference speed, which is crucial for deployment on mobile devices.

Cascaded hierarchical CNN for 2D hand pose estimation from a single color image

Cascaded hierarchical CNN for 2D hand pose estimation from a single color image

Fabrication of Type-Variable Electronic Paper Using Electrophoretic Particle Loading with Multiple Bottom Electrode Structure.

This study suggested the design of type-variable electronic paper with multiple bottom electrode structures and experimentally investigated the process mechanism of the electrophoretic particle loading method (EPLM) as an electronic ink injection method. The type-variable electronic paper was achieved by constructing the multi-electrode structure that had a structure of four electrodes that can independently apply voltage to one cell. By injecting electronic ink that mixes two types of particles with opposite charges into an electrically neutral color (blue) fluid, we realized electronic paper with a single color, and we then measured the optical characteristics of the panel. We used the EPLM to prevent charged particles that have lost their charge from being injected into the e-paper by using an electric field. In order to confirm the color expression and transmittance control effect using the multi-electrode structure, we conducted reflectance measurement and transmittance measurement experiments. Our experiments confirmed that the expression of more than five colors was possible and that the transmittance was controllable to a minimum of 13.50% and a maximum of 71.18%. This study provides an attractive method to create e-paper as a new form outside the framework of existing e-paper technology.

Preparation and characterization of plasticized polyvinyl chloride composite film based on photochromic intelligent cooling

AbstractIndoor temperature adjustment to maintain a relatively comfortable living environment has resulted in different cooling strategies. Often, these strategies are energy consuming as well as energy wasting because of the complex and changing environment. Materials development to achieve intelligent adjustment of indoor temperature through efficient and energy‐saving methods is essential. Here, we present a strategy to prepare polyvinyl chloride (PVC)/photochromic powders cooling films. By adding different colors of photochromic powders to PVC substrate and utilizing their properties of changing color and light transmittance according to sunlight intensity to achieve the intelligent cooling effect. The indoor sunlight simulation test indicates that the PVC/red photochromic powders composite film possesses the best cooling performance under ultraviolet (UV) irradiation, and the maximum cooling range is about 10°C compared with the control sample, meanwhile the maximum cooling effect is only 2–3°C without UV irradiation. The PVC/red photochromic powders composite film also shows the best cooling effect (6°C) in the outdoor solar irradiation test with strong sunlight, and all films did not exhibit an obvious cooling effect under weak sunlight. Meanwhile, PVC/photochromic powders composite films also solve the shortcoming of the single color of traditional cooling films, which can better meet the demand of people for color diversity. In addition, the stable mechanical properties of PVC/photochromic powders composite films also give it great application value. Therefore, PVC/photochromic powders composite films can provide a promising energy‐saving solution for future intelligent adjustment of indoor temperature.

Trap depth engineering in MgGa2O4: Bi3+ for muticolor dynamic anti-counterfeiting, encryption and optical temperature sensing applications

Anti-counterfeiting and encryption are key technologies for information transmission in modern society. Whereas, most optical materials reported only provide single luminous color and fixed response mode, which limits their safety in advanced anti-counterfeiting applications. In this work, we reported a Bi3+-assisted trap depth engineering strategy to achieve a dual-mode dynamic photoluminescence response of MgGa2O4: Bi3+ with intriguing anti-counterfeiting and encryption performances. Upon the ultraviolet (UV) of 254 nm irradiation for 20 s, the color of MgGa2O4: x% Bi3+ (x = 0, 1.0, 2.0, 3.0, 5.0) samples showed dynamic transformation from green or near-infrared to bluish-white emission. Combined with the dynamic photoluminescent (PL) behavior of MgGa2O4: 2.0% Bi3+, high-level dynamic anti-counterfeiting and encryption properties of MgGa2O4: Bi3+ for tagging spies were investigated through powder filling followed by a masked screen printing method. On the other hand, the fluorescence intensity ratios (FIR) of I430 nm/I500 nm and I430 nm/I709 nm for MgGa2O4: 2.0% Bi3+ showed excellent temperature-dependent quenching behaviors in the temperature range of 283 to 393 K, and the relative temperature sensitivities for FIR of I430 nm/I500 nm and I430 nm/I709 nm are 2.18% and 2.98% K−1 at 283 K, respectively. These results inspired more flexible designs to safeguard information security and develop novel high-efficiency optical temperature detectors.

Convincing 3D Face Reconstruction from a Single Color Image under Occluded Scenes

The last few years have witnessed the great success of generative adversarial networks (GANs) in synthesizing high-quality photorealistic face images. Many recent 3D facial texture reconstruction works often pursue higher resolutions and ignore occlusion. We study the problem of detailed 3D facial reconstruction under occluded scenes. This is a challenging problem; currently, the collection of such a large scale high resolution 3D face dataset is still very costly. In this work, we propose a deep learning based approach for detailed 3D face reconstruction that does not require large-scale 3D datasets. Motivated by generative face image inpainting and weakly-supervised 3D deep reconstruction, we propose a complete 3D face model generation method guided by the contour. In our work, the 3D reconstruction framework based on weak supervision can generate convincing 3D models. We further test our method on the MICC, Florence and LFW datasets, showing its strong generalization capacity and superior performance.

PhotoelastNet: a deep convolutional neural network for evaluating the stress field by using a single color photoelasticity image.

Quantifying the stress field induced into a piece when it is loaded is important for engineering areas since it allows the possibility to characterize mechanical behaviors and fails caused by stress. For this task, digital photoelasticity has been highlighted by its visual capability of representing the stress information through images with isochromatic fringe patterns. Unfortunately, demodulating such fringes remains a complicated process that, in some cases, depends on several acquisitions, e.g., pixel-by-pixel comparisons, dynamic conditions of load applications, inconsistence corrections, dependence of users, fringe unwrapping processes, etc. Under these drawbacks and taking advantage of the power results reported on deep learning, such as the fringe unwrapping process, this paper develops a deep convolutional neural network for recovering the stress field wrapped into color fringe patterns acquired through digital photoelasticity studies. Our model relies on an untrained convolutional neural network to accurately demodulate the stress maps by inputting only one single photoelasticity image. We demonstrate that the proposed method faithfully recovers the stress field of complex fringe distributions on simulated images with an averaged performance of 92.41% according to the SSIM metric. With this, experimental cases of a disk and ring under compression were evaluated, achieving an averaged performance of 85% in the SSIM metric. These results, on the one hand, are in concordance with new tendencies in the optic community to deal with complicated problems through machine-learning strategies; on the other hand, it creates a new perspective in digital photoelasticity toward demodulating the stress field for a wider quantity of fringe distributions by requiring one single acquisition.

Classification and detection using hidden Markov model‐support vector machine algorithm based on optimal colour space selection for blood images

Patients with cerebral haemorrhages need to drain haematomas. Fresh blood may appear during the haematoma drainage process, so this needs to be observed and detected in real time. To solve this problem, this paper studies images produced during the haematoma drainage process. A blood image feature selection recognition and classification framework is designed. First, aiming at the characteristics of the small colour differences in blood images, the general RGB colour space feature is not obvious. This study proposes an optimal colour channel selection method. By extracting the colour information from the images, it is recombined into a 3 × 3 matrix. The normalised 4-neighbourhood contrast and variance are calculated for quantitative comparison. The optimised colour channel is selected to overcome the problem of weak features caused by a single colour space. After that, the effective region in the image is intercepted, and the best colour channel of the image in the region is transformed. The first, second and third moments of the three best colour channels are extracted to form a nine-dimensional eigenvector. K-means clustering is used to obtain the image eigenvector, outliers are removed, and the results are then transferred to the hidden Markov model (HMM) and support vector machine (SVM) for classification. After selecting the best color channel, the classification accuracy of HMM-SVM is greatly improved. Compared with other classification algorithms, the proposed method offers great advantages. Experiments show that the recognition accuracy of this method reaches 98.9%.

Multifilter photometry of Solar System objects from the SkyMapper Southern Survey

Context. The populations of small bodies of the Solar System (asteroids, comets, and Kuiper Belt objects) are used to constrain the origin and evolution of the Solar System. Their orbital distribution and composition distribution are both required to track the dynamical pathway from their formation regions to their current locations. Aims. We aim to increase the sample of Solar System objects (SSOs) that have multifilter photometry and compositional taxonomy. Methods. We searched for moving objects in the SkyMapper Southern Survey. We used the predicted SSO positions to extract photometry and astrometry from the SkyMapper frames. We then applied a suite of filters to clean the catalog from false-positive detections. We finally used the near-simultaneous photometry to assign a taxonomic class to objects. Results. We release a catalog of 880 528 individual observations, consisting of 205 515 known and unique SSOs. The catalog completeness is estimated to be about 97% down to V = 18 mag and the purity is higher than 95% for known SSOs. The near-simultaneous photometry provides either three, two, or a single color that we use to classify 117 356 SSOs with a scheme consistent with the widely used Bus-DeMeo taxonomy. Conclusions. The present catalog contributes significantly to the sample of asteroids with known surface properties (about 40% of main-belt asteroids down to an absolute magnitude of 16). We will release more observations of SSOs with future SkyMapper data releases.

ATP binding enhances SWR1 target search by promoting 1D scanning on DNA

One dimensional (1D) target search is a well characterized phenomenon for many DNA binding proteins but is poorly understood for chromatin remodelers. Herein, we characterize the 1D scanning properties of SWR1, a yeast chromatin remodeler that performs histone exchange on the +1 nucleosome which is adjacent to a nucleosome depleted region (NDR) at promoters. Using single and dual color single particle tracking on DNA stretched with optical tweezers, we observe that SWR1 freely scans DNA utilizing a combination of sliding and hopping motions.

A Data Driven Approach to Assess Complex Colour Profiles in Plant Tissues.

The ability to quantify the colour of fruit is extremely important for a number of applied fields including plant breeding, postharvest assessment, and consumer quality assessment. Fruit and other plant organs display highly complex colour patterning. This complexity makes it challenging to compare and contrast colours in an accurate and time efficient manner. Multiple methodologies exist that attempt to digitally quantify colour in complex images but these either require a priori knowledge to assign colours to a particular bin, or fit the colours present within segment of the colour space into a single colour value using a thresholding approach. A major drawback of these methodologies is that, through the process of averaging, they tend to synthetically generate values that may not exist within the context of the original image. As such, to date there are no published methodologies that assess colour patterning using a data driven approach. In this study we present a methodology to acquire and process digital images of biological samples that contain complex colour gradients. The CIE (Commission Internationale de l’Eclairage/International Commission on Illumination) ΔE2000 formula was used to determine the perceptually unique colours (PUC) within images of fruit containing complex colour gradients. This process, on average, resulted in a 98% reduction in colour values from the number of unique colours (UC) in the original image. This data driven procedure summarised the colour data values while maintaining a linear relationship with the normalised colour complexity contained in the total image. A weighted ΔE2000 distance metric was used to generate a distance matrix and facilitated clustering of summarised colour data. Clustering showed that our data driven methodology has the ability to group these complex images into their respective binomial families while maintaining the ability to detect subtle colour differences. This methodology was also able to differentiate closely related images. We provide a high quality set of complex biological images that span the visual spectrum that can be used in future colorimetric research to benchmark colourimetric method development.