Large Color Variations Research Articles

High-Efficiency, Long-Lifetime and Color-Tunable Hybrid WOLEDs Using a Platinum Complex with Voltage-Dependent Monomer and Aggregate Emission.

Color-tunable white organic light-emitting diodes (CT-WOLEDs) have attracted widespread attention given their large color variation to meet the different daily scenarios from the perspective of circadian rhythm. However, most reported CT-WOLEDs, especially the tri-color devices, exhibit poor performances and sophisticated structures. Here, a simple structure tri-color CT-WOLED is demonstrated that simultaneously exhibits high efficiency, ultralong operation lifetime, and wide color-tunable range for dynamic sunlight emulation. The design is based on a newly developed platinum complex that can emit light efficiently in both monomer and aggregation states, providing voltage-dependent green-to-red phosphorescence emission, not only ensuring tunable colors in WOLEDs but also simplifying the device structure. Combining with a blue delayed fluorescence material, this hybrid device exhibits a wide range of tunable colors with Commission Internationale de l'Eclairage 1931 (CIE)coordinatesand correlated color emperature (CCT) shifts from (0.502, 0.474) and 2628 K at 2.6V to (0.211, 0.334) and 14860 K at 8V, achieving good visual alignment with sunlight color throughout the day. This same device also shows high external quantum efficiencies from 28.8% at maximum to 26.2% at 5000cdm-2. Furthermore, an impressively long time of 21,144h is achieved to decay to 90% of the initial luminance at 100cdm-2, being the longest among recorded CT-WOLEDs.

Lite-UNet: A lightweight and efficient network for cell localization

Cell localization constitutes a fundamental research domain within the realm of pathology image analysis, with its core objective being the precise identification of cell spatial coordinates. The task has always involved the challenge of large color variations among cells, uneven distribution, and overlapping borders. Furthermore, in realistic cell localization scenarios, the existing state-of-the-art methods suffer from high computational costs and slow inference times, which severely reduce the efficiency of computer-assisted. To tackle the above issues, a lightweight and efficient cell localization model named Lite-UNet is proposed. Specifically, the Lite-UNet encompasses three pivotal modules. Firstly, we introduce a gradient aggregation module grounded in difference convolution. This module effectively mitigates the challenge posed by extensive color variations among cells by adeptly leveraging gradient information. Secondly, we propose an efficient plug-and-play graph correlation attention module, which optimizes the feature representation capabilities by encoding higher-order feature associations. Finally, we design a lightweight Ghost_CBAM module that alleviates the difficulty of uneven cell distribution while forming the base module of the Lite-UNet. Extensive experiments show that our Lite-UNet is capable of locating cells in images quickly and accurately, thus further improving the efficiency of computer-assisted medicine.

Class Guided Channel Weighting Network for Fine-Grained Semantic Segmentation

Deep learning has achieved promising performance on semantic segmentation, but few works focus on semantic segmentation at the fine-grained level. Fine-grained semantic segmentation requires recognizing and distinguishing hundreds of sub-categories. Due to the high similarity of different sub-categories and large variations in poses, scales, rotations, and color of the same sub-category in the fine-grained image set, the performance of traditional semantic segmentation methods will decline sharply. To alleviate these dilemmas, a new approach, named Class Guided Channel Weighting Network (CGCWNet), is developed in this paper to enable fine-grained semantic segmentation. For the large intra-class variations, we propose a Class Guided Weighting (CGW) module, which learns the image-level fine-grained category probabilities by exploiting second-order feature statistics, and use them as global information to guide semantic segmentation. For the high similarity between different sub-categories, we specially build a Channel Relationship Attention (CRA) module to amplify the distinction of features. Furthermore, a Detail Enhanced Guided Filter (DEGF) module is proposed to refine the boundaries of object masks by using an edge contour cue extracted from the enhanced original image. Experimental results on PASCAL VOC 2012 and six fine-grained image sets show that our proposed CGCWNet has achieved state-of-the-art results.

3D Printed Electrochromic Supercapacitors with Ultrahigh Mechanical Strength and Energy Density.

With the accelerating update of advanced electronic gadgets, a great deal of attention is being paid today to the function integration and intelligent design of electronic devices. Herein, a novel kind of multitasking 3D oxygen-deficient WO3- x ∙ 2H2 O/Ag/ceramic microscaffolds, possessing simultaneous giant energy density, ultrahigh mechanical strength, and reversible electrochromic performance is proposed, and fabricated by a 3D printing technique. The ceramic microscaffolds ensure outstanding mechanical strength and stability, the topology optimized porous lattice structure provides developed surface area for coloration as well as abundant easily accessible channels for rapid ion transportation, and the bifunctional oxygen-defective pseudomaterials enable the large areal capacity and impressive electrochromic performance. As a result, this 3D-printed multitasking microscaffolds simultaneously perform structure-designable, electrochromic, compression resistant, and energy storage functions, behaving with true 3D structure with tailorable curvatures, excellent compressive strength (61.9 MPa), large color variations (>145% in b* value), good aesthetic visual quality as well as exciting electrochemical performances for energy storage including ultrahigh areal capacitance (10.05 F cm-2 at 5 mA cm-2 ), record-high energy density (0.60 mWh cm-2 ), and superior long-term cycling stability (88.6% capacity retention after 10 000 cycles). This work opens up the possibility for high-performance multi-functional coupling structural materials and integrated systems.

Fabrication of gradient anisotropic cellulose hydrogels for applications in micro-strain sensing

A gradient anisotropic cellulose hydrogel was prepared by the diffusion of CaCl2 solution. The degree of orientation of the cellulose chains decreased along the ion diffusion direction, and the birefringence of the highly oriented area was up to 1.323×10−4. Importantly, we first propose and demonstrate the presence of sensitive region in the gradient anisotropy hydrogel. The sensitive region located in the order-disorder transition displayed large color variation with the optical path difference (R) range from 155 nm to 1200 nm, high sensitivity (1 % strain interval), low detection (minimum 1 % strain), good cycling ability of 50 times and frost resistance at −20℃. Based on this, the readable response colorimetric card was designed for micro-strain detection. The programmable Ca2+ diffusion design made it convenient to fabricate cylindrical and tubular hydrogels. This concept of sensitive region and this flexible strategy will broaden new horizons to materials that have excellent responsive properties for optical applications, sensors and multiscale bionics architectures.

Encoding features from multi-layer Gabor filtering for visual smoke recognition

It is a challenging task to accurately recognize smoke from visual scenes due to large variations in smoke shape, color and texture. To improve recognition accuracy, we propose a framework mainly with a robust local feature extraction module based on Gabor convolutional networks. We first propose a Gabor convolutional network, each layer of which mainly consists of Gabor convolution and feature fusion. To fuse feature maps generated by Gabor convolution, we present three Basic Grouping Methods, which divide the feature maps into several groups along orientation axis, scale axis and both of them. To avoid exponential growth of feature maps and preserve discriminative information simultaneously, we propose three element-wise aggregation functions, which are mean, min and max, to combine feature maps in each group. To further improve efficiency, we use local binary pattern to encode hidden and output maps of Gabor convolutional layers. In addition, we use a weight vector to optimize concatenation of histograms for further improvement. Experiments show that our method achieves very outstanding results on smoke, texture and material image datasets. Although the feature extraction step of our method is training-free, our framework consistently outperforms state-of-the-art methods on small smoke datasets, even including deep learning-based methods.

Deep smoke segmentation

Inspired by the recent success of fully convolutional networks (FCN) in semantic segmentation, we propose a deep smoke segmentation network to infer high quality segmentation masks from blurry smoke images. To overcome large variations in texture, color and shape of smoke appearance, we divide the proposed network into coarse and fine paths. The coarse path is an encoder-decoder FCN with skip structures, which extracts global context information of smoke and accordingly generates a coarse segmentation mask. To retain fine spatial details of smoke, the fine path is also designed as an encoder-decoder FCN with skip structures, but it is shallower than the coarse path network. Finally, we propose a very small network containing only addition, convolution and activation layers to fuse the results of the two paths. Thus, we can easily train the proposed network end to end for simultaneous optimization of network parameters. To avoid the great difficulty in manually labelling fuzzy smoke boundaries, we propose a method to generate synthetic smoke images. According to the results of our deep segmentation method, we can easily and accurately perform smoke detection on videos. Experiments on three synthetic smoke datasets and one realistic smoke dataset show that our method achieves much better performance than state-of-the-art segmentation algorithms. Test results of our method on videos are also appealing.

Convolutional neural networks based on multi-scale additive merging layers for visual smoke recognition

Traditional smoke recognition methods are mainly based on handcrafted features. However, it is difficult to design handcrafted features that are robust and discriminative for smoke recognition because of large variations in smoke color, shapes and textures. To solve this problem, we specifically design a basic block of convolutional neural networks (CNNs) and stack basic blocks to propose a novel deep multi-scale CNN (DMCNN) for smoke recognition. The basic block consists of several parallel convolutional layers with the same number of filters but different kernel sizes for scale invariance. Each convolutional layer is followed by a batch normalization to normalize the output of the convolutional layer. Then the basic block sums up all normalized outputs from multi-scale parallel layers and activates the sum as the final output of the block. To fully extract scale invariant features, we cascade eleven basic blocks, which is followed by a global average pooling and a 2D fully connected layer, to construct DMCNN. Experimental results show that our method achieves higher detection rates, higher accuracy rates and lower false alarm rates than existing methods. To further verify the efficiency of DMCNN, we also conducted face gender recognition experiments on the LFW database and our model also achieves obviously higher accuracy rates than other methods. Furthermore, our method is an efficient, lightweight CNN model with about 1 M parameters that are far less than other CNN methods.

Light Field inpainting propagation via Low Rank Matrix completion.

Building up on the advances in low rank matrix completion, this article presents a novel method for propagating the inpainting of the central view of a light field to all the other views. After generating a set of warped versions of the inpainted central view with random homographies, both the original light field views and the warped ones are vectorized and concatenated into a matrix. Because of the redundancy between the views, the matrix satisfies a low rank assumption enabling us to fill the region to inpaint with low rank matrix completion. To this end, a new matrix completion algorithm, better suited to the inpainting application than existing methods, is also developed in this paper. In its simple form, our method does not require any depth prior, unlike most existing light field inpainting algorithms. The method has then been extended to better handle the case where the area to inpaint contains depth discontinuities. In this case, a segmentation map of the different depth layers of the inpainted central view is required. This information is used to warp the depth layers with different homographies. Our experiments with natural light fields captured with plenoptic cameras demonstrate the robustness of the low rank approach to noisy data as well as large color and illumination variations between the views of the light field.

Close-up images of the final Philae landing site on comet 67P/Churyumov-Gerasimenko acquired by the ROLIS camera

After coming to rest on the night side of comet 67P/Churyumov-Gerasimenko, the ROLIS camera on-board Rosetta’s Philae lander acquired five images of the surface below the lander, four of which were with the aid of LED illumination of different colors. The images confirm that Philae was perched on a sloped surface. A local horizon is visible in one corner of the image, beyond which we can see the coma. Having spent a full day on the surface Philae was commanded to lift and rotate, after which a final, sixth, LED image was acquired. The change in perspective allowed us to construct a shape model of the surface. The distance to the foreground was about 80 cm, much larger than the nominal 30 cm. This caused stray light, rather than directly reflected LED light, to dominate the image signal, complicating the analysis. The images show a lumpy surface with a roughness of apparently fractal nature. Its appearance is completely different from that of the first landing site, which was characterized by centimeter to meter-sized debris (Mottola et al., 2015). We recognize neither particles nor pores at the image resolution of 0.8 mm per pixel and large color variations are absent. The surface has a bi-modal brightness distribution that can be interpreted in terms of the degree of consolidation, a hypothesis that we support with experimental evidence. We propose the surface below the lander to consist of smooth, cracked plates with unconsolidated edges, similar to terrain seen in CIVA images.

A Major Locus for Quantitatively Measured Shank Skin Color Traits in Korean Native Chicken

Shank skin color of Korean native chicken (KNC) shows large color variations. It varies from white, yellow, green, bluish or grey to black, whilst in the majority of European breeds the shanks are typically yellow-colored. Three shank skin color-related traits (i.e., lightness [L*], redness [a*], and yellowness [b*]) were measured by a spectrophotometer in 585 progeny from 68 nuclear families in the KNC resource population. We performed genome scan linkage analysis to identify loci that affect quantitatively measured shank skin color traits in KNC. All these birds were genotyped with 167 DNA markers located throughout the 26 autosomes. The SOLAR program was used to conduct multipoint variance-component quantitative trait locus (QTL) analyses. We detected a major QTL that affects b* value (logarithm of odds [LOD] = 47.5, p = 1.60×10−49) on GGA24 (GGA for Gallus gallus). At the same location, we also detected a QTL that influences a* value (LOD = 14.2, p = 6.14×10−16). Additionally, beta-carotene dioxygenase 2 (BCDO2), the obvious positional candidate gene under the linkage peaks on GGA24, was investigated by the two association tests: i.e., measured genotype association (MGA) and quantitative transmission disequilibrium test (QTDT). Significant associations were detected between BCDO2 g.9367 A>C and a* (PMGA = 1.69×10−28; PQTDT = 2.40×10−25). The strongest associations were between BCDO2 g.9367 A>C and b* (PMGA = 3.56×10−66; PQTDT = 1.68×10−65). However, linkage analyses conditional on the single nucleotide polymorphism indicated that other functional variants should exist. Taken together, we demonstrate for the first time the linkage and association between the BCDO2 locus on GGA24 and quantitatively measured shank skin color traits in KNC.

Spatial distributions of local illumination color in natural scenes

In natural complex environments, the elevation of the sun and the presence of occluding objects and mutual reflections cause variations in the spectral composition of the local illumination across time and location. Unlike the changes in time and their consequences for color appearance and constancy, the spatial variations of local illumination color in natural scenes have received relatively little attention. The aim of the present work was to characterize these spatial variations by spectral imaging. Hyperspectral radiance images were obtained from 30 rural and urban scenes in which neutral probe spheres were embedded. The spectra of the local illumination at 17 sample points on each sphere in each scene were extracted and a total of 1904 chromaticity coordinates and correlated color temperatures (CCTs) derived. Maximum differences in chromaticities over spheres and over scenes were similar. When data were pooled over scenes, CCTs ranged from 3000K to 20,000K, a variation of the same order of magnitude as that occurring over the day. Any mechanisms that underlie stable surface color perception in natural scenes need to accommodate these large spatial variations in local illumination color.

Development of a Hyperspectral Imaging System for the Early Detection of Apple Rottenness Caused by Penicillium

AbstractAutomatic detection of early rottenness caused by Penicillium is still a challenge due to the high similarity between the rotten region and sound tissue, and the large variations in surface background color of apples. A hyperspectral reflectance imaging system and a systematic approach were developed to detect the early rottenness caused by Penicillium on Fuji apples. The principal component analysis of the hyperspectral reflectance images in the full region (400–1,000 nm) of spectra was used to develop hyperspectral image analytical algorithm and select the efficient wavelengths. Independent component analysis (ICA) based on the selected efficient wavelengths (810 and 970 nm, respectively) and simple thresholding methods were used to segment and classify the rottenness from the sound tissue. Finally, ICA‐based image processing and classification algorithm was developed based on the images at 810 and 970 nm to realize the fast inspection of early rottenness. The detection result with 97% overall accuracy indicated that the proposed system and algorithm were efficient and suitable for the detection of the early rottenness caused by Penicillium on Fuji apples. This study was the first attempt to use hyperspectral imaging system to detect the early rottenness caused by Penicillium on Fuji apples and would lay a foundation for the development of multispectral imaging system for fast inspection of rottenness. The limitation of our research is the limited inspection view of the samples. Future work will be focused on simultaneous whole surface inspection using multispectral imaging system.Practical ApplicationsThe algorithm and results in our paper can provide a foundational basis to develop a real‐time inspection system to detect rottenness on apples in the postharvest processing line, or develop a monitoring system to monitor the quality changes in storage shelf.

Colorless, transparent, dye-doped polymer films exhibiting tunable luminescence color: controlling the dual-color luminescence of 2-(2'-hydroxyphenyl)imidazo[1,2-a]pyridine derivatives with the surrounding matrix.

Colorless, transparent, polymer films including 2-(2'-hydroxyphenyl)imidazo[1,2-a]pyridine (HPIP) derivatives, 1 and 2, are prepared by a spin-coating method. The observed emission spectra upon photoexcitation of these polymer films were composed of dual emission bands: the normal luminescence at 370-410 nm (purple) and the greatly Stokes-shifted emission at 520-580 nm (yellow) assigned as the excited-state intramolecular proton transfer (ESIPT) luminescence. Relative intensity of the two emissions varied according to the polymer matrices, resulting in change in the luminescent color of the dye-doped polymer films. Particularly, the luminescence properties of 6-cyano HPIP, 2, are highly susceptible to the surrounding environment, and therefore successfully tuned to produce a wide range of colors, from purple to orange, by changing its concentration within and the type of the polymer matrix. This observation can be ascribed to the formation of a relatively weak intramolecular hydrogen bond resulting from the electron-withdrawing 6-cyano group. Thus, we demonstrate large variations in emission color can be achieved using interactions of the single component with the surrounding matrix. These results offer promise as a convenient and effective method for a wide-range tuning the luminescence colors of dye-doped polymer films.

Isochromatic lines as extension of Helmholtz reciprocity principle for effect paints.

Flake-based parameters were recently introduced as a physical concept to predict a series of measurement geometries producing similar reflection data for effect paints. We derive expressions to calculate these so-called isochromatic lines, connecting the two Helmholtz-reciprocal in-plane geometries with a series of out-of-plane geometries. Thus isochromatic lines can be regarded as an extension of the Helmholtz reciprocity principle, which is valid for effect paints. We experimentally studied seven effect paint samples with large angular color variation along the length of four isochromatic lines. A change in illumination angles by up to 75° while following isochromatic lines led to a standard deviation in color parameters of less than two units. When isochromatic lines were not followed, these colorimetric parameters varied by more than 10 units already by change in detection angle of 10°. Therefore the concept of isochromatic lines works well for effect paints.

Detection of large color variation in the potentially hazardous asteroid (297274) 1996 SK

Low-inclination near-earth asteroid (NEA) (297274) 1996 SK, which is also classified as a potentially hazardous asteroid, has a highly eccentric orbit. It was studied by multi-wavelength photometry within the framework of an NEA color survey at Lulin Observatory. Here, we report the finding of large color variation across the surface of (297274) 1996 SK within one asteroidal rotation period of 4.656 ± 0.122 hours and classify it as an S-type asteroid according to its average colors of B — V = 0.767 ± 0.033, V — R = 0.482 ± 0.021, V — I = 0.801 ± 0.025 and the corresponding relative reflectance spectrum. These results might be indicative of differential space weathering or compositional inhomogeneity in the surface materials.

A New Log Gabor Approach for Text Detection from Video

Text information embedded in video frames play an important role in content-based multimedia indexing and retrieval, as a result the automatic detection of texts from videos has gained wide attention in recent years. However,large variations in size, color, orientation and background complexity makes video text detection and extraction a challenging problem. In this paper an effective method for text detection and localization approach based on Log Gabor filter and Block Eigen map analysis is proposed. Log Gabor filter response is used to identify candidate text regions and Block wise Eigen map analysis is used to classify pixels as true text pixels. Finally,a heuristic based on gray scale pixel co-occurrence to eliminate false positives from the frame is used. Experimental results show the promising overall performance of the proposed method against the best features reported in the literature for the standard databases.

A Texture-Based Local Soft Voting Method for Vanishing Point Detection from a Single Road Image

SUMMARY Estimating a proper location of vanishing point from a single road image without any prior known camera parameters is a challenging problem due to limited information from the input image. Most edge-based methods for vanishing point detection only work well for structured roads with clear painted lines or distinct boundaries, while they usually fail in unstructured roads lacking sharply defined, smoothly curving edges. In order to overcome this limitation, texture-based methods for vanishing point detection have been widely published. Authors of these methods often calculate the texture orientation at every pixel of the road image by using directional filter banks such as Gabor wavelet filter, and seek the vanishing point by a voting scheme. A local adaptive soft voting method for obtaining the vanishing point was proposed in a previous study. Although this method is more effective and faster than prior texture-based methods, the associated computational cost is still high due to a large number of scanning pixels. On the other hand, this method leads to an estimation error in some images, in which the radius of the proposed half-disk voting region is not large enough. The goal of this paper is to reduce the computational cost and improve the performance of the algorithm. Therefore, we propose a novel local soft voting method, in which the number of scanning pixels is much reduced, and a new vanishing point candidate region is introduced to improve the estimation accuracy. The proposed method has been implemented and tested on 1000 road images which contain large variations in color, texture, lighting condition and surrounding environment. The experimental results demonstrate that this new voting method is both efficient and effective in detecting the vanishing point from a single road image and requires much less computational cost when compared to the previous voting method.

New efficient vanishing point detection from a single road image based on intrinsic line orientation and color texture properties

Detecting the vanishing point from a single road image is a challenging problem because there is very limited information in the input image that can help the computer to deduce the genuine location of vanishing point. Besides, the cluttered ambient environment in a real road image sometimes will hinder rather than assist the detection. Learning both the advantages and the limitations of current edge-based and texture-based approaches motivates us to propose a new vanishing point detection method that exploits the intrinsic geometric line structures and color texture properties of general roads. Our approach integrates the efficiency of line segments of edge-based methods, and the orientation coherence concept that is frequently applied in texture-based methods, which can be of great help to improve the accuracy of selecting the right line segments for vanishing point detection. The proposed method has been implemented and tested on over 1000 various road images. These road images exhibit large variations in color, texture, illumination condition, and ambient environment. The experimental results demonstrate that this new method is both efficient and effective in detecting vanishing point when compared to the state-of-the-art edge-based and texture-based methods.

Effects of Flour Extraction Rate, Added Water, and Salt on Color and Texture of Chinese White Noodles

ABSTRACTBoth cultivar and noodle composition and preparation have important effects on noodle quality. In this study, the effects of flour extraction rate (50, 60, and 70%), added water (33, 35, and 37%), and salt concentration (0, 1, and 2%, w/w) on color and texture of Chinese white noodle (CWN) were investigated using flour samples from five leading Chinese wheat cultivars. The five samples showed large variations in protein content, ash content, flour color, farinograph, and extensigraph parameters, and starch pasting properties. Analyses of variance indicated that cultivar, flour extraction rate, level of water addition, salt concentration, and the interactions had significant effects on color of raw noodle sheets and color and textural properties of CWN. Cultivar and water addition were more important sources of variation than flour extraction rate and salt concentration. The brightness (L*) and redness (a*) values of raw noodle sheets were significantly reduced and increased, respectively, as flour extraction rate increased from 50 to 70%, and noodle scores were slightly higher at flour extraction rates of 50%. Water addition showed different effects on raw noodle sheet color at 2 and 24 hr, and a significant improvement was observed for noodle appearance, firmness, viscoelasticity, smoothness, and total score as water addition increased from 33 to 37%. L* of raw noodle sheets, and firmness and viscoelasticity of cooked noodles, were significantly improved, but noodle flavor significantly deteriorated as salt concentration increased from 0 to 2%; 1% salt produced the highest noodle score. Thus, the recommended composition for laboratory preparation of CWN is 60% flour extraction, 35% water addition, and 1% salt concentration.