Original Texture Research Articles

Estimating Rangeland Fine Fuel Biomass in Western Texas Using High-Resolution Aerial Imagery and Machine Learning

Rangeland fine fuel biomass is a key factor in determining fire spread and intensity, while the accuracy of biomass estimation is limited due to inherent heterogeneity in rangeland ecosystems. In this study, high spatial resolution (0.23 m) images were used to classify fuel types and predict rangeland fine fuel biomass in west Texas based on the random forest algorithm. Two biomass models, including one with the fuel type, original spectral bands, and vegetation indices as explanatory variables, and the other that contained a combination of the fuel type, original spectral bands, vegetation, and texture indices as explanatory variables, were assessed. Furthermore, the biomass models were also examined by upscaling the remote sensing images from high to medium (30 m) spatial resolution with the spectral curves derived from Landsat images. The fuel type map had an accuracy of more than 95%, and herbaceous fuel types were kept for estimating fine fuel biomass. The results showed that around 76% and 80% of biomass variances were explained by models without texture indices and with texture indices, respectively. The fuel type and the normalized difference vegetation index (NDVI) were two significant input variables influencing fine fuel biomass for both models and adding texture indices contributed to the improvement of model accuracy. An upscaling analysis for biomass estimation using medium spatial resolution imagery showed that approximately 60% of the variance in biomass was explained by the model. The addition of fractional vegetation cover improved the model performance by explaining an additional 5% of the variance in biomass estimation. These findings indicate that high spatial resolution images have the potential to effectively estimate rangeland fuel types and fine fuel biomass, which can be helpful for mapping the spatial distribution of fine fuels to aid in monitoring and fire management on rangelands.

Artificial neural network modeling for predicting surface texture and its attenuation of micro-surfacing containing steel slag aggregates

• Artificial neural network models of micro-surfacing texture were established. • The effect of various factors on original texture and its attenuation were evaluated. • The traffic cycles and aggregate morphology has significant impact on surface texture. • Steel slag could improve the original texture and delay the attenuation of texture. For the research gap that the texture and its attenuation laws of micro-surfacing containing steel slag have not been considered, the objectives of this research were to establish the relationship between long-term texture features and engineering parameters using artificial neural network, and to determine the influence degree of each parameter. The results show that gradation and angularity are the most influential inputs for macro and micro-texture, respectively. The superior wear resistance of steel slag can delay the attenuation of micro-texture. The results of this study could provide alternatives to predict surface texture and offer sustainable way to recycle steel slag.

Palaeoproterozoic arc related supracrustal units from the Tasiilaq Region, SE Greenland: Insights into the convergence of the Rae and North Atlantic Cratons

The Tasiilaq region in SE Greenland contains Palaeoproterozoic supracrustal rocks, which record the collision and suturing of the Rae Craton and North Atlantic Craton during the 1.9 Ga Nagssugtoqidian orogeny. The original mineralogy and textures of the supracrustal rocks have been largely overprinted by amphibolite facies metamorphism, obscuring the original protoliths and complicating the interpretation of the early Earth surface environment in which these rocks formed. Here, we present new major and trace element data for the Schweizerland, Kuummiut and Isertoq Terranes, with the former two representing the Rae craton, and the latter the northern margin of the North Atlantic Craton. Supracrustal lithologies include metapelites, marbles, calc-silicate rocks and mafic to felsic volcanic rocks and/or shallow intrusive rocks. Pelites normalised to Post Archaean Australian shales have (sub)horizontal rare earth element patterns, with La/SmCN: 1.61 – 23.91 and Eu/Eu* 0.34 – 1.83 and Th/Sc 1. The volcanic rocks are enriched relative to the primitive mantle (PM) with parallel to sub parallel PM-normalised heavy rare earth elements. They have a suprasubduction zone signature with distinct Nb-Ta troughs. The amphibolites can be further divided into those that interacted with the crust; back-arc basalts with subtle Nb-Ta troughs; and fore-arc basalts with distinct Nb-Ta and Zr-Hf troughs and negative Ti anomalies. The marbles consist of a Mg-rich dolomite group with δ13C 0.48 to 0.91 ‰ and δ18O −6.13 to −7.57 ‰; and Mg-poor calcitic carbonates with δ13C −0.74 to 0.17 ‰ and δ18O −10.81 to −14.87 ‰. This isotopic composition suggests formation at ∼ 2050 Ma during the low δ13C excursion following the Great Oxidation Event. We propose that the supracrustals of the Tasiilaq region were deposited at 2050 Ma in a series of interconnected basins during dual subduction convergence of the North Atlantic and Rae cratons, with development of an island arc with back arc basin in the north, and a continental arc in the south. These basins were inverted during continental collision, thrust, and folded into the Archaean Kuummiut and Isertoq TTG-dominated terrane basement, before being subjected to the region-wide amphibolite facies metamorphism at ∼ 1840 Ma during the Nagssugtoqidian orogeny.

Super-Resolution Thermal Imaging Using Uncooled Infrared Sensors for Non-Destructive Testing of Adhesively Bonded Joints

To compensate for the inability of inexpensive uncooled infrared (IR) sensors to perform high-speed thermography detection with low noise and high sensitivity in the field of non-destructive testing (NDT), a post-processing method for uncooled IR sensors based on compressive sensing is proposed in this paper to perform super-resolution NDT thermography. Super-resolution IR phase images can be generated by first using a sparse representation of the low-resolution (LR) IR images and a sparse dictionary generated from randomly sampled high-resolution (HR) raw training image patches to generate an HR IR image sequence, and, subsequently, implementing IR thermography NDT. To verify the reliability of the proposed method, super-resolution IR images were combined with pulsed phase thermography (PPT) to verify bonded joint structures in carbon fibre-reinforced polymer (CFRP). Compared to other methods, the proposed method reduces the mean square error (MSE) by 11.13% and yields a better visual effect on the PPT phase image reconstructed using three-fold super-resolution. The proposed method not only produces sharp defect edges, but also retains the original texture, which is expected to enable wider application of uncooled IR sensors in NDT applications in the future.

Light-Modulated Rheological Properties in Green Innovative Formulations

The addition of azorubine to a viscoelastic aqueous dispersion of sodium oleate (NaOL, 0.43 M, 13% w/w) and KCl (up to 4% w/w) leads to a green gel-like system whose rheological behavior can be efficiently and reversibly triggered from remote by using UV light. Rheology, Differential Scanning Calorimetry (DSC) measurements and phase behavior studies indicate that the original texture of the NaOL dispersion is significantly hardened upon UV irradiation for 8 hours in the presence of azorubine, showing a seven hundred-fold increase in viscosity. The UV treatment brings about the trans to cis isomerization of azorubine, which modifies the structure of the NaOL wormlike micellar system, leading to a more entangled, close-textured network. The cooperative effect of KCl on the fluid viscosity is found to be concentration-dependent. The system slowly reverts to its original rheological behaviour after standing for about 1 day. These results are relevant for the development of stimuli-responsive innovative systems based on biocompatible, non expensive and commercially available materials that can be used in a wide range of applications, such as in drug delivery or enhanced oil recovery, where a quick change in the physico-chemical features of the system is required but difficult to be performed.

DRSNFuse: Deep Residual Shrinkage Network for Infrared and Visible Image Fusion.

Infrared images are robust against illumination variation and disguises, containing the sharp edge contours of objects. Visible images are enriched with texture details. Infrared and visible image fusion seeks to obtain high-quality images, keeping the advantages of source images. This paper proposes an object-aware image fusion method based on a deep residual shrinkage network, termed as DRSNFuse. DRSNFuse exploits residual shrinkage blocks for image fusion and introduces a deeper network in infrared and visible image fusion tasks than existing methods based on fully convolutional networks. The deeper network can effectively extract semantic information, while the residual shrinkage blocks maintain the texture information throughout the whole network. The residual shrinkage blocks adapt a channel-wise attention mechanism to the fusion task, enabling feature map channels to focus on objects and backgrounds separately. A novel image fusion loss function is proposed to obtain better fusion performance and suppress artifacts. DRSNFuse trained with the proposed loss function can generate fused images with fewer artifacts and more original textures, which also satisfy the human visual system. Experiments show that our method has better fusion results than mainstream methods through quantitative comparison and obtains fused images with brighter targets, sharper edge contours, richer details, and fewer artifacts.

Cittaslow Movement as a Tool to Strengthen the City Image: Case Study of Osmaneli (Bilecik)

Intense urbanization movements, unconscious usage of energy sources, along with rural-urban migration based on various reasons and rapid population growth are the common problems of today’s cities. The natural results of such problems can be summarized as the emergence of environments that are naked of aesthetics, changes in the habits of urban-dwellers with regards to their life styles and cultures, along with the negative impacts on the life quality of people. With this study, it is aimed to analyze the suitability of Osmaneli town to be a part of the Cittaslow Movement through the questionnaires to be conducted, along with identifying the physical effects of participating in this movement on the development of Osmaneli’s urban image, and developing planning & design suggestions. The historical and natural landscape elements of Osmaneli surviving to date, along with its characteristics that strengthen the city image, are analyzed within the scope of the study. Field surveys have been carried out within the scope of the study method. In this direction, questionnaire study was carried out in order to detect the elements that bear the city image characteristics and brand value, while also measuring the participation and compliance rate of the public in case of becoming a part of Cittaslow Movement. A city that wants to join the Slow City Movement must meet certain conditions and undertake to provide others. Osmaneli settlement is in a lucky position in this context. Since it is a small settlement in Bilecik, it was not affected much by urban interventions and preserved its original texture. In addition, strengthening the city image of Osmaneli and increasing its tourism potential will provide economic development to the city. In this context, various determinations and suggestions have been developed and a tourism route has been created for the identity elements of the city.

Study of Texture Indicators Applied to Pavement Wear Analysis Based on 3D Image Technology.

Pavement texture characteristics can reflect early performance decay, skid resistance, and other information. However, most statistical texture indicators cannot express this difference. This study adopts 3D image camera equipment to collect texture data from laboratory asphalt mixture specimens and actual pavement. A pre-processing method was carried out, including data standardisation, slope correction, missing value and outlier processing, and envelope processing. Then the texture data were calculated based on texture separation, texture power spectrum, grey level co-occurrence matrix, and fractal theory to acquire six leading texture indicators and eight extended indicators. The Pearson correlation coefficient was used to analyse the correlation of different texture indicators. The distinction vector based on the information entropy is calculated to analyse the distinction of the indicators. High correlations between ENE (energy) and ENT (entropy), ENT and D (Minkowski dimension) were found. The CON (contrast) has low correlations with HT (macro-texture power spectrum area), ENT and D. However, the differentiation of ENE and HT is more prominent, and the differentiation of the CON is smaller. ENE, ENT, CON and D indicators based on macro-texture and the corresponding original texture have strong linear correlations. However, the microtexture indicators are not linearly correlated with the corresponding original texture indicators. D, WT (micro-texture power spectrum area) and ENT exhibit high degrees of numerical concentration for the same road sections and may be more statistically helpful in distinguishing the characteristics of the pavement performance decay of the road sections.

Naturalistic Approach for Management of Fractured Anterior Teeth – A Case Report

Dental trauma accounts for about 5% of all injuries in children and adolescents. Anterior teeth are most frequently affected by trauma, especially the maxillary incisors. Various treatment options are available like light cure composite resin restoration, fragment reattachment, and ceramic crowns, laminate veneers. Among those, fragment reattachment is a conservative method. The procedure is simple and economic and needs less chair-side time and also provide good aesthetics, because of the maintenance of original morphology, color, and surface texture. It also allows to restore the tooth with minimal loss of the remaining tooth structure, which in turn maintains the integrity of the teeth. The success of fragment reattachment also depends on patient and parent cooperation, adequate recall and follow up of the patient to monitor the pulp vitality. This clinical case reports the management of two coronal tooth fracture cases that were successfully treated using tooth fragment reattachment using flowable composite.

Reconstruction of Traditional Village Spatial Texture Based on Parametric Analysis

Chinese traditional villages, as the space carriers of Chinese excellent traditional culture, are a nonrenewable cultural heritage with rich value connotations. The primary consideration for the protection of the authenticity of traditional villages as a historical and cultural heritage is the continuation of spatial authenticity. The inheritance and shaping of traditional characteristics requires the study of the laws of traditional villages’ spatial form. Spatial texture is an important landscape resource for traditional villages, and it is also a space carrier to display and carry the historical and cultural characteristics of the village. However, the current practice of village planning and construction has destroyed this original spatial texture, resulting in a rupture of the inheritance of traditional characteristics of village space. The fundamental reason lies in the lack of emphasis on the subjectivity and regularity of village space and the lack of quantitative analysis and planning application of the inherent laws of spatial texture. Based on the integration of inheritance and transformation, this research proposes to use the advantages of digitization, visualization, and dynamics in parametric technology to quantitatively analyze the internal laws of the village spatial texture and assist the planning and design of the village spatial texture, in order to effectively promote the benign inheritance and optimization of the traditional characteristics of the village spatial texture which makes the planned spatial texture both traditional and modern. The research results show that (1) the parametric-based planning technology can better analyze the existing laws of the village spatial texture in a comprehensive, quantitative, and precise manner. (2) The parametric technology has a relatively high value in the planning and design of the village spatial texture. Due to strong applicability, it can invert the spatial form of the village and optimize the planning scheme by adjusting parameters and rules. (3) Although the parametric planning and design party has certain artificial intelligence, it cannot completely replace the human brain. The planning scheme generated by the method cannot be directly applied but can be used to assist the design scheme. It needs to be adjusted and corrected via the subjective initiative of people and wider participation.

Digital Camouflage Pattern Design Based on the Biased Random Walk

Digital camouflage is a common countermeasure against military reconnaissance. In the face of high-tech imaging reconnaissance, battlefield detection means tend to be automated and refined. In order to adapt to the concealment requirements under various environmental backgrounds, combined with the camouflage performance of digital camouflage and its feedback mechanism in camouflage pattern design, this paper proposed a digital camouflage pattern design method based on biased random walk. Firstly, the original background is preprocessed, and the background texture’s direction, corner, step length, and pixel intensity difference are statistically analyzed, and the boundary probability between pixel nodes is estimated. Then, a biased random walk is used to outline the camouflage patches. The edge scatter is enriched according to the density of the patches, and the camouflage patches are filled according to the proportion of the main color of the background. Finally, a digital camouflage pattern is obtained. The quantitative analysis results show that the mean heart rate of the digital camouflage pattern based on multiscene design is at least 31.0% higher than that of the original background segmentation texture, and the standard deviation index of equivalent diameter is increased by 14.9% on average. In addition, the results of simulation camouflage image detection in multiple scenes show that the proposed method can effectively deal with camouflage target detection on the basis of fully retaining the original background texture information and has strong camouflage concealment effect in the scene.

Automatic defect detection of texture surface with an efficient texture removal network

Automated defect inspection of texture surface is still a challenging task in the industrial automation field due to the tremendous changes in the appearance of various surface textures. We present a simple but powerful image transformation network to remove textures and highlight defects at full resolution. The simple full convolution network consists only of 3 × 3 regular convolution and several dilated convolution blocks, which makes it compact and able to capture multi-scale features effectively. To further improve the ability of the network to suppress texture and highlight defects, a polynomial loss function combining perceptual loss, structural similarity loss and image gradient loss is proposed. In addition, a semi-automatic annotation method mainly composed of wavelet transform and relative total variation is designed to generate a data set of image pairs containing the original texture image and the desired texture removal image. We conducted experiments on a milled metal surface defect dataset and an open data set containing various textured backgrounds to evaluate the performance of our method. Compared with other convolutional neural network approaches, the results demonstrate the superiority of the proposed method. The method has been applied to the surface defect online detection system of an aluminum ingot milling production line, which effectively improves the surface inspection efficiency and product quality.

Wearable fabric-based hybrid energy harvester from body motion and body heat

We demonstrate a wearable fabric-based energy harvester (Fab-EH) to hybridize a triboelectric generator (TrG) and a thermoelectric generator (ThG). Using a liquid-phase aluminum (Al) coating technique, we fabricate Al-coated fabrics with high electrical and thermal conductivity while maintaining the original texture. By adopting the Al-coated fabric material as both the electrodes of the TrG and the heat-transport layer of the ThG, the fabricated Fab-EH effectively scavenges electrical energy from body motion and body heat. The Fab-EH charges a storage capacitor with capacitance of 3.3 mF to 3 V within 240 sec with the designed transforming system. Moreover, a smartphone can be partially charged using the harvested energy. The Fab-EH thus has the potential to alleviate the recharging issue for portable and wearable devices.

Method for generating masks on face images and systems for their recognition

The problem of masked face recognition is investigated. It is shown that real masks of various shapes, textures and colors have become a problem for state-of-the-art face recognition systems. A reason for this is the lack of the necessary real training datasets. Creation of new data based on simple methods of forming masks on face images could solve this problem. An original method is proposed including the generation of various types, shapes, and colors of masks directly on the original texture of face images. The formation of the masks on the faces of individuals, on faces in group photos, and in scenes with streams of people was taken into account. Based on 100 original face images from the CUHK Face Sketch Database, a test database was created that includes more than 20,000 masked faces images which available for use. Experiments were carried out to recognize faces from the test database within the implemented four systems, among which three are state-of-the-art systems based on “deep learning” and one is deterministic system based on the cosine-transform. The performance of these systems was evaluated, the obtained results of masked face recognition were interpreted, and the masks that were a problem for selected four systems were noted. The proposed mask generation method can be used to create corpora and test databases of images with masks. The obtained results will be useful to researchers and specialists in the field of image processing and analysis.

Experimental Design Sensory Quality Boba Developed From Pumpkin

In this day and age, boba topping is one of the most popular toppings for selling drinks, especially bubble tea. The boba itself was discovered in 1987 in Taiwan but has only recently become popular. Researchers want to make innovations from boba by adding pumpkin as the main ingredient of boba. Pumpkin has many benefits, so it is excellent and safe to use as a topping. In this study, researchers wanted to know about the original boba's color, taste, texture, and aroma, which would change if the essential pumpkin ingredient was added. This research is experimental. The researcher used an organoleptic test consisting of a hedonic test and a hedonic quality test to determine the panelists' preferences and good and bad impressions of the boba made. The researcher distributed the product to 20 trained panelists to be given an assessment. The results showed that the comparison of preferences between authentic boba and boba using pumpkin as the primary ingredient was not much different. The products made are still favored by the panelists. There are also some significant changes to the boba in color, taste, texture, and aroma.

An improved algorithm for superresolution reconstruction of ancient murals with a generative adversarial network based on asymmetric pyramid modules

Ancient Chinese murals are true portrayals of ancient Chinese life, but well-preserved murals are rare. Therefore, ancient mural preservation and repair are critical. To address the poor superresolution reconstruction of mural images with unclear textures and fuzzy details, we developed an improved generative adversarial network (GAN) algorithm based on asymmetric pyramid modules for ancient mural superresolution reconstruction. Asymmetric pyramid modules, which are composed of a series of dense compression units, were used to learn image features. To analyze the reconstructed image features, a perceptual loss function was integrated to optimize the model performance. The use of the improved algorithm for low-resolution mural images increased the image resolution while preserving their original feature details and textures, and the improvement effect was visually observed in terms of indices such as the peak signal-to-noise ratio and structural similarity. Compared with other superresolution-related algorithms, the proposed model increased the peak signal-to-noise ratio by 0.20–6.66 dB. The GAN-based mural superresolution reconstruction algorithm proposed in this study effectively improved the performance of reconstructed high-resolution mural images, which increases the significance of the reconstructed image for future research.

Chondritic breccias: An optical microscopic classification

Numerous meteorites are breccias, indicating asteroid collisions. Herein we systematically investigate brecciated chondrites to efficiently and quickly classify large numbers of breccias that are housed in organizations such as the National Institute of Polar Research (NIPR). We observed 1024 chondrites in the NIPR collection only by using optical microscopy. Many terms have been proposed to describe breccias. In order to classify breccias in a short time, we propose a determinative chart only by optical microscopy from terminology previously proposed and our observations. Fragmental breccias consist of primordial lithology as a clast or a host among fragmental fine-grained minerals. If the lithology is apparently consistent throughout, it is considered a monomict breccia. Otherwise, it is provisionally called a fragmental breccia. Breccias can be classified according to evidence of in situ melting. Melt breccias contain unmelted clasts, which preserve the original chondritic textures, whereas melt rocks do not contain such clasts and represent nearly total melting.

Research on facial expression recognition based on an improved fusion algorithm

Abstract This article puts forward a facial expression recognition (FER) algorithm based on multi-feature fusion and convolutional neural network (CNN) to solve the problem that FER is susceptible to interference factors such as non-uniform illumination, thereby reducing the recognition rate of facial expressions. It starts by extracting the multi-layer representation information (asymmetric region local binary pattern [AR-LBP]) of facial expression images and cascading them to minimize the loss of facial expression texture information. In addition, an improved algorithm called divided local directional pattern (DLDP) is used to extract the original facial expression image features, which not only retains the original texture information but also reduces the time consumption. With a weighted fusion of the features extracted from the above two facial expressions, new AR-LBP-DLDP facial local features are then obtained. Later, CNN is used to extract global features of facial expressions, and the local features of AR-LBP-DLDP obtained by weighted fusion are cascaded and fused with the global features extracted by the CNN, thereby producing the final facial expression features. Ultimately, the final facial expression features are input into Softmax for training and classification. The results show that the proposed algorithm, with good robustness and real-time performance, effectively improves the recognition rate of facial expressions.

WGAN-Based Image Denoising Algorithm

Traditional image denoising algorithms are generally based on spatial domains or transform domains to denoise and smooth the image. The denoised images are not exhaustive, and the depth-of-learning algorithm has better denoising effect and performs well while retaining the original image texture details such as edge characters. In order to enhance denoising capability of images by the restoration of texture details and noise reduction, this article proposes a network model based on the Wasserstein GAN. In the generator, small convolution size is used to extract image features with noise. The extracted image features are denoised, fused and reconstructed into denoised images. A new residual network is proposed to improve the noise removal effect. In the confrontation training, different loss functions are proposed in this paper.

MCW-Net: Single image deraining with multi-level connections and wide regional non-local blocks

A recent line of convolutional neural network-based works has succeeded in capturing rain streaks. However, difficulties in detailed recovery still remain. In this paper, we present a multi-level connection and wide regional non-local block network (MCW-Net) to properly restore the original background textures in rainy images. Unlike existing encoder–decoder-based image deraining models that improve performance with additional branches, MCW-Net improves performance by maximizing information utilization without additional branches through the following two proposed methods. The first method is a multi-level connection that repeatedly connects multi-level features of the encoder network to the decoder network. Multi-level connection encourages the decoding process to use the feature information of all levels. In multi-level connection, channel-wise attention is considered to learn which level of features is important in the decoding process of the current level. The second method is a wide regional non-local block. As rain streaks primarily exhibit a vertical distribution, we divide the grid of the image into horizontally-wide patches and apply a non-local operation to each region to explore the rich rain-free background information. Experimental results on both synthetic and real-world rainy datasets demonstrate that the proposed model significantly outperforms existing state-of-the-art models. Furthermore, the results of the joint deraining and segmentation experiment prove that our model contributes effectively to other vision tasks.