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Water stress has been one of the most serious threat to the crop growth, development and yield quality in agricultural fields. Timely and accurate diagnosis of crop water stress can greatly contribute to the precision irrigation for the crop resilience and yield. In this study, the research object was taken from the summer maize in the typical dryland agricultural area of northwest China. A six-channel multispectral sensor was mounted on a drone to obtain the remote sensing image data of summer maize at the nodulatione and staminate pulling stage in 2022. At the same time, the stomatal conductance and phenotypic parameters of summer maize were also collected. The background was removed by supervised classification. The canopy vegetation index and image texture were obtained using the gray-scale covariance matrix. The sensitive vegetation index, image texture and phenotypic parameters and their combinations were screened out by the Bayesian information criterion and full subset filtering. The summer maize stomatal conductance estimation model was constructed to combine the three types of machine learnings: the extreme learning machine, the random forest, and the back-propagation neural network. The optimal model was mapped to estimate the stomatal conductance. The Pearson correlation coefficient of vegetation index and stomatal conductance were significantly positively correlated, whereas, the canopy reflectance of summer maize was weakly negatively correlated. Different types of image textures at different wavelengths were correlated with the stomatal conductance, and the highest correlation was found in the 550 nm band. The Pearson correlation coefficients between morphological structure phenotypes (plant height, stem thickness and leaf area) and stomatal conductance of summer maize were 0.72, 0.58 and 0.69, respectively, where the three types of phenotypic parameters data were correlated well with stomatal conductance. Vegetation indices with spectral reflectance data were used to assess the overall health and moisture status of the vegetation. Image texture was used to capture the spatial distribution, texture and structural features of crops. Crop phenotypic parameters were then used to reflect the physiological and morphological responses of the crop in a three-dimensional manner, providing visual information about the growth and moisture of the vegetation. The decision coefficients of the crop water stress diagnostic models that constructed from the three information sources increased from 0.728 and 0.750 to 0.841, respectively, compared with the single or two combinations, indicating the great potential to stomatal conductance prediction. The optimal combination of indicators was screened by Bayesian information criterion and full subset screening: DWSI, NDVI, MEA, ENT, plant height and leaf area. The back-propagation neural network model with the three complementary information sources was the optimal model for the water stress diagnosis of summer maize (coefficient of determination of 0.841, root mean square error of 0.043 mol/(m2 ·s), and mean absolute error of 0.034 mol/(m² ·s)). The underestimation of stomatal conductance was significantly improved, compared with the rest models. The inverse map with the optimal model was widely applied to easily and accurately diagnose the crop water stress for the purpose of irrigation strategies and resource allocation. The finding can provide a feasible and accurate diagnosis of water stress in summer maize. [ABSTRACT FROM AUTHOR]

The detection and monitoring of burned areas is crucial for vegetation recovery, loss assessment, and anomaly analysis. Although vegetation indices (VIs) have been widely used, accurate vegetation detection is challenging due to potential confusion in the spectra of different types of land cover and the interference of shadow effects caused by terrain. In this work, a novel Vegetation Anomaly Spectral Texture Index (VASTI) is proposed, which leverages the merits of both spectral and spatial texture features to identify abnormal pixels for extracting burned vegetation areas. The performance of the VASTI and its components, the Global Environmental Monitoring Index (GEMI), the Enhanced Vegetation Index (EVI), and the texture feature Autocorrelation (AC) were assessed based on a global dataset previously established, which contains 1774 pairs of samples from 10 different sites. The results illustrated that, compared with the GEMI and EVI, the VASTI improved the user's accuracy (UA), producer's accuracy (PA), and kappa coefficient across the ten study areas by approximately 5% to 10%. Compared to AC, the VASTI improved the accuracy of abnormal vegetation detection by 13% to 25%. The improvements were mainly caused by the fact that the incorporation of texture features can reduce spectral confusion between pixels. The innovation of the VASTI is that it considers the relationship between anomalous pixels and surrounding pixels by explicitly integrating spatial texture features with traditional spectral features. [ABSTRACT FROM AUTHOR]

With the rapid growth in the number of motor vehicles worldwide, the general public is beginning to attach importance to the quality inspection of wheels before they leave the factory. The current wheel defect detection systems are often cumbersome to operate and have low practical performance. Therefore, this research will use dynamic image segmentation, image texture feature extraction and Back Propagation neural network classification based on wheel image defect feature analysis algorithm to achieve automatic intelligent detection of automotive wheel defects. In this study, an intelligent detection system for automotive wheel defects is also designed, and finally the performance of the detection system is tested experimentally to illustrate its practicality. The experimental results show that the proposed intelligent detection system for automotive wheel defects based on image texture features identifies defects in wheel castings with a correct rate of 96% and a false positive rate of only 2%. This illustrates that the detection system proposed in this study has a high recognition rate and can provide a useful reference for the automotive industry inspection. [ABSTRACT FROM AUTHOR]

The microtexture of the asphalt pavement, which is critical for the skid resistance, is dictated by the microtexture of the coarse aggregates used in the surface courses. The existing standard test methods can only capture the combined measure of coarse aggregate surface texture which can be influenced by other aggregate characteristics such as shape and angularity. Consequently, the surface texture of aggregates cannot be quantified explicitly using these tests, and therefore image analysis methods that can extract textural features are gaining more significance. In the present study, various image texture analysis methods are evaluated to identify a reliable texture indicator. Methods include traditional statistical approaches such as histogram method, Grey Level Co-occurrence Matrix (GLCM) methods, and transform-based wavelet texture analysis. The image analysis was carried out on the grey-scale scanning electron microscope (SEM) images before and after accelerated polishing. To compare the image texture parameters, the average surface roughness (Sa) measured from aggregate surface profile data obtained from a 3D Optical Surface Profilometer (3D OSP) was considered as a direct measure of surface texture. A detailed correlation analysis with Sa indicates that the surface texture index (STI) from the wavelet analysis quantifies the microtexture more accurately compared to statistical methods. [ABSTRACT FROM AUTHOR]

In conjunction with pandemics, medical image data are growing exponentially. In some countries, hospitals collect biometric data from patients, such as fingerprints, iris, or faces. This data can be used for things like identity verification and security management. However, this medical data can be easily compromised by hackers. In order to prevent illegal tampering with medical images and invasion of privacy, a new texture fusion medical image encryption (TFMIE) algorithm derived from biometric images is proposed, which can encrypt the image using biometric information for storage or transmission. First, the medical image is decomposed into n-bit-planes by bit-plane decomposition. Secondly, a fusion image is generated by a biometric image with a circular local binary pattern and pixel-weighted average method. The fused image is further decomposed into n bit-planes through bit-plane decomposition and performs XOR operation with the original medical image in reverse order. Following the execution of the XOR operation, a new scrambling and diffusion algorithm based on a one-dimensional fractional trigonometric function (1DFTF) chaotic map is employed to form the cipher image. The experimental results show that compared with the existing methods, the average information entropy value of TFMIE is 7.99, and the average values of NPCR and UACI reach 0.9958 and 0.3346, respectively, which have strong key sensitivity, good robustness, and anti-attack ability. The method is lossless and has high transmission efficiency, which can meet the needs of medical big data encryption. [ABSTRACT FROM AUTHOR]

Gels combined with honey might generate new possibilities of textures in food development. This work explores the structural and functional properties of gelatin (5 g/100 g), pectin (1 g/100 g), and carrageenan (1 g/100 g) gels with different content of honey (0–50 g/100 g). Honey decreased the transparency of gels and made them more yellow‐greenish; all of them were firm and uniform, especially at the highest honey content. The water holding capacity increased (63.30–97.90 g/100 g) and moisture content, water activity (0.987–0.884) and syneresis (36.03–1.30 g/100 g) decreased with the addition of honey. This ingredient modified mainly the textural parameters of gelatin (Hardness: 0.82–1.35 N) and carrageenan gels (Hardness: 2.46–2.81 N), whereas only the adhesiveness and the liquid like‐behavior were increased in the pectin gels. Honey increased the solid behavior of gelatin gels (G': 54.64–173.37 Pa) but did not modify the rheological parameters of the carrageenan ones. Honey also had a smoothing effect on the microstructure of gels as observed in the scanning electron microscopy micrographs. This effect was also confirmed by the results of the gray level co‐occurrence matrix and fractal model's analysis (fractal dimension: 1.797–1.527; lacunarity: 1.687–0.322). The principal component and cluster analysis classified samples by the hydrocolloid used, except the gelatin gel with the highest content of honey, which was differentiated as a separate group. Honey modified the texture, rheology, and microstructure of gels indicating that it is possible to generate new products to be used in other food matrices as texturizers. [ABSTRACT FROM AUTHOR]

Accurate and reliable mapping of fire extent and severity is critical for assessing the impact of fire on vegetation and informing post-fire recovery trajectories. Classification approaches that combine pixel-wise and neighbourhood statistics including image texture derived from high-resolution satellite data may improve on current methods of fire severity mapping. Texture is an innate property of all land cover surfaces that is known to vary between fire severity classes, becoming increasingly more homogenous as fire severity increases. In this study, we compared candidate backscatter and reflectance indices derived from Sentinel 1 and Sentinel 2, respectively, together with grey-level-co-occurrence-matrix (GLCM)-derived texture indices using a random forest supervised classification framework. Cross-validation (for which the target fire was excluded in training) and target-trained (for which the target fire was included in training) models were compared to evaluate performance between the models with and without texture indices. The results indicated that the addition of texture indices increased the classification accuracies of severity for both sensor types, with the greatest improvements in the high severity class (23.3%) for the Sentinel 1 and the moderate severity class (17.4%) for the Sentinel 2 target-trained models. The target-trained models consistently outperformed the cross-validation models, especially with regard to Sentinel 1, emphasising the importance of local training data in capturing post-fire variation in different forest types and severity classes. The Sentinel 2 models more accurately estimated fire extent and were improved with the addition of texture indices (3.2%). Optical sensor data yielded better results than C-band synthetic aperture radar (SAR) data with respect to distinguishing fire severity and extent. Successful detection using C-band data was linked to significant structural change in the canopy (i.e., partial-complete canopy consumption) and is more successful over sparse, low-biomass forest. Future research will investigate the sensitivity of longer-wavelength (L-band) SAR regarding fire severity estimation and the potential for an integrated fire-mapping system that incorporates both active and passive remote sensing to detect and monitor changes in vegetation cover and structure. [ABSTRACT FROM AUTHOR]

To achieve the rapid recognition and accurate picking of Camellia oleifera fruits, a binocular vision system composed of two industrial cameras was used to collect images of Camellia oleifera fruits in natural environments. The YOLOv7 convolutional neural network model was used for iterative training, and the optimal weight model was selected to recognize the images and obtain the anchor frame region of the Camellia oleifera fruits. The local binary pattern (LBP) maps of the anchor frame region were extracted and matched by using the normalized correlation coefficient template matching algorithm to obtain the positions of the center point in the left and right images. The recognition experimental results showed that the accuracy rate, recall rate, mAP and F1 of the model were 97.3%, 97.6%, 97.7% and 97.4%. The recognition rate of the Camellia oleifera fruit with slight shading was 93.13%, and the recognition rate with severe shading was 75.21%. The recognition rate of the Camellia oleifera fruit was 90.64% under sunlight condition, and the recognition rate was 91.34% under shading condition. The orchard experiment results showed that, in the depth range of 400–600 mm, the maximum error value of the binocular stereo vision system in the depth direction was 4.279 mm, and the standard deviation was 1.142 mm. The detection and three-dimensional positioning accuracy of the binocular stereo vision system for Camellia oleifera fruits could basically meet the working requirements of the Camellia oleifera fruit-picking robot. [ABSTRACT FROM AUTHOR]

Objective: To study the clinical application value of enhanced computed tomography (CT) image texture parameters combined with serum carcinoembryonic antigen (CEA) and carbohydrate antigen 724 (CA724) in the recurrence and metastasis of esophageal cancer after operation. Methods: 86 patients with esophageal cancer who underwent operation treatment in our hospital from June 2019 to July 2021 were selected. The serum CEA and CA724 levels were detected before operation, and enhanced CT scanning was performed, the image data of the arteriovenous phase were analyzed by texture, and the mean value, median, standard deviation, unevenness, skewness, kurtosis, entropy and other parameters were recorded. The patients with esophageal cancer were followed up for 12 months after operation. According to whether they had recurrence and metastasis, they were divided into recurrence and metastasis group (n=32) and non-recurrence and metastasis group (n=54). The differences of texture parameters of enhanced CT images and serum CEA and CA724 levels in the two groups were compared. Receiver Operating characteristic (ROC) curve was used to analyze the predictive value of enhanced CT image texture parameters combined with serum CEA and CA724 for postoperative recurrence and metastasis of patients with esophageal cancer. Results: The arterial stage entropy in the recurrence and metastasis group was higher than that in the non-recurrence and metastasis group (P<0.05). The inevenness and entropy of venous phase in the recurrence and metastasis group were higher than those in the non-recurrence and metastasis group(P<0.05). The serum CEA and CA724 levels in the recurrence and metastasis group were higher than those in the non-recurrence and metastasis group (P<0.05). ROC curve showed that the combination of serum CEA and CA724 had significantly improved the arterial phase entropy, the venous phase unevenness, the area under the curve (AUC) of the entropy, the sensitivity and the specificity compared with the single application of each index, and the combined application had higher predictive efficiency. Conclusion: The serum CEA and CA724 levels and the increase of the entropy and the inhomogeneity and the arterial phase entropy and the inhomogeneity and the entropy of venous phase are related to the recurrence and metastasis of the postoperative patients with esophageal cancer. The combination of the arterial phase entropy and the inhomogeneity and the entropy of venous phase with the serum CEA and CA724 has of high application value in predicting the recurrence and metastasis of the postoperative patients with esophageal cancer. [ABSTRACT FROM AUTHOR]

The generation of Digital Elevation Models (DEMs) through stereogrammetry of optical satellite images has gained great popularity across various disciplines. For the analysis of these DEMs, it is important to understand the influence of the input data and different processing steps and parameters employed during stereo correlation. Here, we explore the effects that image texture, as well as the use of different matching algorithms (Block Matching (BM) and More Global Matching (MGM)), can have on optical DEMs derived from the flexible, open-source Ames Stereo Pipeline. Our analysis relies on a ∼2700 km 2 clip of a SPOT6 tristereo scene covering the hyperarid, vegetation-free Pocitos Basin and adjacent mountain ranges in the northwestern Argentine Andes. A large, perfectly flat salt pan (paleolake bed) that covers the center of this basin is characterized by strong contrasts in image texture, providing a unique opportunity to quantitatively study the relationship between image texture and DEM quality unaffected by topography. Our findings suggest that higher image texture, measured by panchromatic variance, leads to lower DEM uncertainty. This improvement continues up to ∼10 3 panchromatic variance, above which further improvements in DEM quality are independent of local image texture but instead may have sensor or geometric origins. Based on this behavior, we propose that image texture may serve as an important proxy of DEM quality prior to stereo correlation and can help to set adequate processing parameters. With respect to matching algorithms, we observe that MGM improves matching in low-texture areas and overall generates a smoother surface that still preserves complex, narrow (i.e., ridge and valley) features. Based on this sharper representation of the landscape, we conclude that MGM should be preferred for geomorphic applications relying on stereo-derived DEMs. However, we note that the correlation kernel selected for stereo-matching must be carefully chosen depending on local image texture, whereby larger kernels generate more accurate matches (less artifacts) at the cost of smoothing results. Overall, our analysis suggests a path forward for the processing and fusion of overlapping satellite images with suitable view-angle differences to improve final DEMs. [ABSTRACT FROM AUTHOR]

Background: The placental pathological changes in hypertensive disorders of pregnancy (HDP) starts early in pregnancy, the deep convolutional neural networks (CNN) can identify these changes before its clinical manifestation. Objective: To compare the placental quantitative ultrasound image texture of women with HDP to those with the normal outcome. Methods: The cases were enrolled in the first trimester of pregnancy, good quality images of the placenta were taken serially in the first, second, and third trimester of pregnancy. The women were followed till delivery, those with normal outcomes were controls, and those with HDP were cases. The images were processed and classified using validated deep learning tools. Results: Total of 429 cases were fully followed till delivery, 58 of them had HDP (13.5%). In the first trimester, there was a significant difference in the placental length (p = .033), uterine artery PI (p = .019), biomarkers PAPP-A (p = .001) PlGF (p = .013) and placental image texture (p = .001) between the cases and controls. In the second trimester the uterine artery PI, serum PAPPA (p = .010) and PlGF (p = .005) levels were significantly low among women who developed hypertension later on pregnancy. The image texture disparity between the two groups was highly significant (p < .001). The model "resnext 101_32x8d" had Cohen kappa score of 0.413 (moderate) and the accuracy score of 0.710 (good). In the first trimester the best sensitivity and specificity was observed for abnormal placental image texture (70.6% and 76.6%, respectively) followed by PlGF (64% and 50%, respectively), in the second trimester the abnormal image texture had the highest sensitivity and specificity (60.4% and 73.3%, respectively) followed by uterine artery PI (58.6% and 54.7%, respectively). Similarly in the third trimester, uterine artery PI had sensitivity and specificity of 60.3% and specificity of 50.7%, whereas the abnormal image texture had sensitivity and specificity of 83.5%. Conclusion: Ultrasound placental analysis using artificial intelligence (UPAAI) is a promising technique, would open avenues for more research in this field. [ABSTRACT FROM AUTHOR]

Monitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed so far, image-based methods, which are less invasive, nondestructive, and more biosecure, are practically preferred. Nevertheless, the premise behind most of those approaches is simply averaging the pixel values of images as inputs of a regression model to predict density values, which may not provide rich information of the microalgae presenting in the images. In this work, we propose to exploit more advanced texture features extracted from captured images, including confidence intervals of means of pixel values, powers of spatial frequencies presenting in images, and entropies accounting for pixel distribution. These diverse features can provide more information of microalgae, which can lead to more accurate estimation results. More importantly, we propose to use the texture features as inputs of a data-driven model based on L1 regularization, called least absolute shrinkage and selection operator (LASSO), where their coefficients are optimized in a manner that prioritizes more informative features. The LASSO model was then employed to efficiently estimate the density of microalgae presenting in a new image. The proposed approach was validated in real-world experiments monitoring the Chlorella vulgaris microalgae strain, where the obtained results demonstrate its outperformance compared with other methods. More specifically, the average error in the estimation obtained by the proposed approach is 1.54, whereas those obtained by the Gaussian process and gray-scale-based methods are 2.16 and 3.68, respectively [ABSTRACT FROM AUTHOR]

Background: Tumor histomorphology analysis plays a crucial role in predicting the prognosis of resectable lung adenocarcinoma (LUAD). Computer-extracted image texture features have been previously shown to be correlated with outcome. However, a comprehensive, quantitative, and interpretable predictor remains to be developed. Methods: In this multi-center study, we included patients with resectable LUAD from four independent cohorts. An automated pipeline was designed for extracting texture features from the tumor region in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) at multiple magnifications. A multi-scale pathology image texture signature (MPIS) was constructed with the discriminative texture features in terms of overall survival (OS) selected by the LASSO method. The prognostic value of MPIS for OS was evaluated through univariable and multivariable analysis in the discovery set (n = 111) and the three external validation sets (V1, n = 115; V2, n = 116; and V3, n = 246). We constructed a Cox proportional hazards model incorporating clinicopathological variables and MPIS to assess whether MPIS could improve prognostic stratification. We also performed histo-genomics analysis to explore the associations between texture features and biological pathways. Results: A set of eight texture features was selected to construct MPIS. In multivariable analysis, a higher MPIS was associated with significantly worse OS in the discovery set (HR 5.32, 95%CI 1.72–16.44; P = 0.0037) and the three external validation sets (V1: HR 2.63, 95%CI 1.10–6.29, P = 0.0292; V2: HR 2.99, 95%CI 1.34–6.66, P = 0.0075; V3: HR 1.93, 95%CI 1.15–3.23, P = 0.0125). The model that integrated clinicopathological variables and MPIS had better discrimination for OS compared to the clinicopathological variables-based model in the discovery set (C-index, 0.837 vs. 0.798) and the three external validation sets (V1: 0.704 vs. 0.679; V2: 0.728 vs. 0.666; V3: 0.696 vs. 0.669). Furthermore, the identified texture features were associated with biological pathways, such as cytokine activity, structural constituent of cytoskeleton, and extracellular matrix structural constituent. Conclusions: MPIS was an independent prognostic biomarker that was robust and interpretable. Integration of MPIS with clinicopathological variables improved prognostic stratification in resectable LUAD and might help enhance the quality of individualized postoperative care. [ABSTRACT FROM AUTHOR]

Tongue texture analysis is of importance to inspection diagnosis in traditional Chinese medicine (TCM), which has great application and irreplaceable value. The tough and tender classification for tongue image relies mainly on image texture of tongue body. However, texture discontinuity adversely affects the classification of the tough and tender tongue classification. In order to promote the accuracy and robustness of tongue texture analysis, a novel tongue image texture classification method based on image inpainting and convolutional neural network is proposed. Firstly, Gaussian mixture model is applied to separate the tongue coating and body. In order to exclude the interference of tongue coating on tough and tender tongue classification, a tongue body image inpainting model is built based on generative image inpainting with contextual attention to realize the inpainting of the tongue body image to ensure the continuity of texture and color change of tongue body image. Finally, the classification model of the tough and tender tongue inpainting image based on ResNet101 residual network is used to train and test. The experimental results show that the proposed method achieves better classification results compared with the existing methods of texture classification of tongue image and provides a new idea for tough and tender tongue classification. [ABSTRACT FROM AUTHOR]

Purpose: The noninvasive monitoring of mild hyperthermia or thermal ablation is important to guarantee therapeutic safety and efficacy. The potential of ultrasound B-mode image texture features in monitoring temperature or coagulation zones studied in this article. Materials and methods: The experiments carried out on eighteen in vitro porcine liver samples heated from 20°C to 60°C in the water bath. The ultrasound radiofrequency signal at different temperature collected to reconstruct B-mode ultrasound images. The texture features based on gray level histogram (GLH), gray level co-occurrence matrix (GLCM), and gray level-gradient co-occurrence matrix (GGCM) extracted, respectively. Accordingly, we analyze the correlations between these texture features and temperature based on the experiment results. Results: The results showed that five texture feature parameters closely related to temperature, including mean gray scale of GLH, homogeneity of GLCM, hybrid entropy, inverse difference moment, and correlation of GGCM. Some of these feature parameters have correlation coefficients larger than 0.9 within the temperature range of 20°C to 60°C. Conclusions: The above-mentioned five feature parameters expected to apply for noninvasive monitoring of MH or TA. [ABSTRACT FROM AUTHOR]

We provide sea ice classification maps of a sub-weekly time series of single (horizontal–horizontal, HH) polarization X-band TerraSAR-X scanning synthetic aperture radar (TSX SC) images from November 2019 to March 2020, covering the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This classified time series benefits from the wide spatial coverage and relatively high spatial resolution of TSX SC data and is a useful basic dataset for future MOSAiC studies on physical sea ice processes and ocean and climate modeling. Sea ice is classified into leads, young ice with different backscatter intensities, and first-year ice (FYI) or multiyear ice (MYI) with different degrees of deformation. We establish the per-class incidence angle (IA) dependencies of TSX SC intensities and gray-level co-occurrence matrix (GLCM) textures and use a classifier that corrects for the class-specific decreasing backscatter with increasing IAs, with both HH intensities and textures as input features. Optimal parameters for texture calculation are derived to achieve good class separation while maintaining maximum spatial detail and minimizing textural collinearity. Class probabilities yielded by the classifier are adjusted by Markov random field contextual smoothing to produce classification results. The texture-based classification process yields an average overall accuracy of 83.70 % and good correspondence to geometric ice surface roughness derived from in situ ice thickness measurements (correspondence consistently close to or higher than 80 %). A positive logarithmic relationship is found between geometric ice surface roughness and TSX SC HH backscatter intensity, similar to previous C- and L-band studies. Areal fractions of classes representing ice openings (leads and young ice) show prominent increases in middle to late November 2019 and March 2020, corresponding well to ice-opening time series derived from in situ data in this study and those derived from satellite synthetic aperture radar (SAR) and optical data in other MOSAiC studies. [ABSTRACT FROM AUTHOR]

In haptic interaction technology, texture haptic display is an important part. In order to perceive color image texture better, the haptic display methods of color texture based on force feedback and tactile feedback are proposed in this work. On the one hand, through the study of the physiological and psychological perception characteristics of color information, a new force rendering method of color image texture based on force feedback device is presented. The experimental results of color texture force perception show that the color texture force rendering algorithm in this paper works well. On the other hand, a color texture vibration tactile model is established based on the designed vibrotactile device. Its effectiveness is verified by vibration tactile perception experiment. Finally, for the color texture image of the real object surface, the force and vibrotactile display methods of color texture in this paper are used to conduct perception experiments and compared. [ABSTRACT FROM AUTHOR]

To a certain extent, whether a banknote can continue to circulate depends on how dirty it is. Therefor, a multi-layer support vector machines (MLSVMs) recognition method based on regional image texture features and threshold selection was proposed. Firstly, the contact image sensor (CIS) is used to collect the double-sided reflection gray-scale images of banknotes under blue light, green light, red light, infrared light and ultraviolet light, and the gray-scale images under green light transmission and infrared light transmission. Secondly, according to the pattern distribution of banknote images, the collected banknote image is divided into 8 areas with different sizes, and 22 texture feature parameters, such as entropy, dissimilarity and correlation of the banknote image, are extracted based on the gray-level co-occurrence matrix (GLCM) to describe the visual features of banknotes dirty degree. Then the 22 texture features by using GLCM under different light sources in different regions are selected through thresholds. Finally, MLSVMs are used to recognition the dirty degree of banknotes, and the simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The purpose of image smoothing is to improve the visual effect of the image and improve the clarity of the image, so as to make the image more conducive to computer processing and various feature analysis. Because the current technology fails to smooth the preprocessed image, it leads to the extraction of image texture features. The anti-interference performance is weak. For this reason, an image texture feature extraction technology based on the digital twin is proposed. Similarity analysis is carried out through the internal structure of the image, and the image is smoothed by the semisupervised learning method. On the basis of optimizing the denoised image through digital twinning, detect target feature points in the original image, then remove the abnormal and split feature points, assign the direction of image texture feature points, and build a fuzzy back propagation neural network model. Image texture feature extraction technology is implemented. The experimental results show that, compared with the traditional method, the proposed technique has a strong identification of original image features, and has a strong consistency with original data, and has a strong ability to resist the influence of abnormal data, noise, or redundant feature points. [ABSTRACT FROM AUTHOR]

In China, cemented paste backfill (CPB) is a common treatment method after the exploitation of basic energy. The homogeneity of slurry influences the performance of CPB. However, the online monitoring and characterization of homogeneity lack relevant technologies and unified standards. This article discusses an online image analysis technique applied to the online monitoring of cemented paste backfill mixing, which is based on the evolution of the texture of images taken at the surface of the mixing bed. First, the grayscale distribution of the image obtained by the high-speed camera in the CPB preparation process was analyzed by Matlab and its variance (s2) was solved, and the texture features of the image were analyzed by the variance of grayscale distribution. Then, a homogeneity discriminant model ( c s t ) was established. The results show that the variance value of the grayscale distribution of the slurry image increases rapidly at first, then gradually decreases, and becomes stable in the final stage since it turns a constant value. When the s2 value tends to be stable, the slurry gradually reaches homogenization, and the discriminant coefficient of paste homogenization based on the homogenization discriminant model reaches 0.05. The homogenization prediction of CPB proves to be consistent with the backfill performance comparison results. The evolution of the texture allows obtaining important information on the evolution of different formulations during mixing, which can be used for intelligent monitoring of CPB preparation process. [ABSTRACT FROM AUTHOR]

Carbon fiber plain-woven prepreg is one of the basic materials in the field of composite material design and manufacturing, in which defect identification is an important and easily neglected part of testing. Here, a novel high recognition rate inspection method for carbon fiber plain-woven prepregs is proposed for inspecting bubble and wrinkle defects based on image texture feature compression. The proposed method attempts to divide the image into non-overlapping block lattices as texture primitives and compress them into a binary feature matrix. Texture features are extracted using a gray level co-occurrence matrix. The defect types are further defined according to texture features by k-means clustering. The performance is evaluated in some existing computer vision and machine learning methods based on fiber recognition. By comparing the result, an overall recognition rate of 0.944 is achieved, which is competitive with the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Equine odontoclastic tooth resorption and hypercementosis (EOTRH) is one of the horses' dental diseases, mainly affecting the incisor teeth. An increase in the incidence of aged horses and a painful progressive course of the disease create the need for improved early diagnosis. Besides clinical findings, EOTRH recognition is based on the typical radiographic findings, including levels of dental resorption and hypercementosis. This study aimed to introduce digital processing methods to equine dental radiographic images and identify texture features changing with disease progression. The radiographs of maxillary incisor teeth from 80 horses were obtained. Each incisor was annotated by separate masks and clinically classified as 0, 1, 2, or 3 EOTRH degrees. Images were filtered by Mean, Median, Normalize, Bilateral, Binomial, CurvatureFlow, LaplacianSharpening, DiscreteGaussian, and SmoothingRecursiveGaussian filters independently, and 93 features of image texture were extracted using First Order Statistics (FOS), Gray Level Co-occurrence Matrix (GLCM), Neighbouring Gray Tone Difference Matrix (NGTDM), Gray Level Dependence Matrix (GLDM), Gray Level Run Length Matrix (GLRLM), and Gray Level Size Zone Matrix (GLSZM) approaches. The most informative processing was selected. GLCM and GLRLM return the most favorable features for the quantitative evaluation of radiographic signs of the EOTRH syndrome, which may be supported by filtering by filters improving the edge delimitation. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the usefulness of machine learning based on image texture parameters to discriminate plum stone cultivars. The plums of cultivars 'Emper', 'Kalipso', and 'Polinka' were sampled. For each cultivar, one hundred images of plum stones were acquired using a digital camera. Processing of the plum stone images included the conversion of the images to individual color channels, image segmentation, region of interest (ROI) determination, and texture parameter extraction. Then, the discriminant analysis, including the texture selection and building discriminative models for the evaluation of the diversity of the plum stone cultivars, was carried out. The obtained results of discrimination of plum stone cultivars were very accurate and confirmed the effectiveness of image processing to evaluate the cultivar diversity. The most satisfactory results, reaching 96.67% for the average accuracy for three cultivars (97% for 'Emper', 'Kalipso', and 96% for 'Polinka'), were obtained for the models built based on combined textures selected from all the color channels using the IBk classifier. The developed procedure can be of practical importance for the correct identification of plum stone cultivars and avoiding their mixing to preserve cultivar uniformity. [ABSTRACT FROM AUTHOR]

Land cover mapping is an important part of resource management, planning, and economic predictions. Improvements in remote sensing, machine learning, image processing, and object based image analysis (OBIA) has made the process of identifying land cover types increasingly faster and reliable but these advances have not been able to utilize all of the information encompassed within ultra-high (sub-meter) resolution imagery. There have been few known attempts to try and maximize this detailed information in high resolution imagery using advanced textural components. Hierarchical land classes are also rarely used as an attribute within the machine learning step of object-based image analysis. In this study we try to circumnavigate the inherent problems associated with high resolution imagery by combining well researched data transformations that aid the OBIA process with a seldom used texture transformation in Geographic Object Based Image Analyses (GEOBIA/OBIA) known as the Gabor Transform and the hierarchal organization of landscapes. We will observe the difference made in segmentation and classification accuracy of a random forest classifier when we fuse a Gabor transformed image to a Normalized Difference Vegetation Index (NDVI), high resolution multi-spectral imagery (RGB and NIR) and Light Detection and Ranging (LiDAR) derived canopy height model (CHM) within a riparian area in Southeast Iowa, United States. Additionally, we will observe the effects on classification accuracy when adding multi-scale land cover data to objects. Both, the addition of hierarchical information and Gabor textural information, could aid the GEOBIA process in delineating and classifying the same objects that human experts would delineate within this riparian landscape. [ABSTRACT FROM AUTHOR]

The use of innovative techniques in the classification of dried products for food processing and packaging systems simplifies and brings objectivity to the related processes. In addition, it helps consumers to access quality products. This study aimed to discriminate dried carrot samples subjected to 12 different combinations of drying and pretreatment by using innovative techniques based on chromatic attributes. The drying procedure consists of three different microwave powers (100, 200, 300 W), and pretreatments include 5% gum arabic solution, 5% gum sucrose solution, ultrasound application, and a control (no-pretreatment). The R , G , B , L , a , b , H , S, and V color channels were extracted from dried carrot images, and the exploration of classification models from selected color channels was an innovative aspect of the study. In all color channels, high overall accuracies were obtained with the values of 96.33, 96.50, and 97.00% for the logistic model tree, k-nearest neighbor, and random forest, respectively. The confusion matrix showed the lowest mixing among carrots dried at H-US (300 W + ultrasound) vs. H-C (300 W + control), H-S (300 W + sucrose) vs. H-C (300 W + control), and M-S (200 W + sucrose) vs. H-S (300 W + sucrose). The highest L, a, and b color values for the dried carrot slices were obtained as M-S (75.26), H-GA (30.93), and M-GA (44.75), respectively. In addition, hue values varied between 17.26 and 13.27, whereas saturation values varied between 50.93 and 59.90. The finding revealed that assessment of the effect of pretreatment and drying on carrot slices could be performed by machine learning and image analysis in a practical, rapid, and non-destructive manner. • Eight different machine learning algorithms was applied for classification. • 12 carrot classes subjected to various treatments were distinguished. • Innovative models built based on image textures were developed. • LMT, kNN, and RF was more successful for classification of dried carrot slices. • The developed procedure was non-destructive, objective, and effective. [ABSTRACT FROM AUTHOR]

The use of ultrasound images to acquire breast cancer diagnosis information without invasion can reduce the physical and psychological pain of breast cancer patients and is of great significance for the diagnosis and treatment of breast cancer. There are some differences in the texture of breast cancer between benign and malignant cases. Therefore, this paper proposes an adaptive learning method based on ultrasonic image texture features to identify breast cancer. Specifically, firstly, we used dictionary learning and sparse representation to learn the ultrasonic image texture dictionary of benign and malignant cases, respectively, and then used the combination of the two dictionaries to represent the test image to obtain the texture distribution characteristics of the test image under the two dictionary representations, which called the sparse representation coefficient. Finally, these above features were filtered by sparse representation and sent to sparse representation classifier to establish benign and malignant classification model. 128 cases were randomly divided into training and testing sets according to 2: 1 for training and testing. The proposed method has achieved state-of-the-art results, with an accuracy of 0.9070 and the area under the receiver operating characteristic curve of 0.9459. The results demonstrate that the proposed method has the potential to be used in the clinical diagnosis of benign and malignant breast cancer. [ABSTRACT FROM AUTHOR]

In order to improve the retrieval ability of super-resolution multi-space block images, a texture retrieval method of block images based on depth hash is proposed. A texture feature analysis model of super-resolution multi-dimensional partitioned images is constructed, which combines texture spatial structure mapping method to realize depth information fusion of partitioned images, adopts edge feature detection and texture sparse feature clustering to realize texture hierarchical structure feature decomposition of super-resolution multi-dimensional partitioned images, and adopts deep image parameter analysis method to construct pixel structure recombination model of multi-dimensional partitioned images. Multi-dimensional texture parameter structure analysis and information clustering are realized for the collected partitioned images in multi-dimensional space. According to the information clustering results, the texture retrieval and extraction of partitioned images are realized by using the deep hash fusion algorithm, and the information detection and feature recognition capabilities of partitioned images in multi-dimensional space are improved. Simulation results show that this method has higher precision and better feature resolution in texture retrieval of partitioned images in multidimensional space, which improves the texture retrieval and recognition ability of partitioned images. [ABSTRACT FROM AUTHOR]

We present a new self-supervised monocular depth estimation method with multi-scale texture detail enhancement. Based on the observation that the image texture detail and the semantic information have essential significance on the depth estimation, we propose to provide them to the network to learn more sharpness and structural integrity of depth. Firstly, we generate the filtered images and detail images by multi-scale decomposition and use a deep neural network to automatically learn their weights to construct the texture detail enhanced image. Then, we consider the semantic features by putting deep features from the VGG-19 network into a self-attention network, guide the depth decoder network to focus on the integrity of objects in the scene. Finally, we propose a scale-invariant smooth loss to improve the structural integrity of the predicted depth. We evaluate our method on the KITTI 2015 and Make3D datasets and apply the predicted depth to novel view synthesis. The experimental results show that it has achieved satisfactory results compared with the existing methods. [ABSTRACT FROM AUTHOR]

The rendering effect of known visual image texture is poor and the output image is not always clear. To solve this problem, this paper proposes a visual image rendering based on scene visual understanding algorithm. In this approach, the color segmentation of known visual scene is carried out according to a predefined threshold, and the segmented image is processed by morphology. For this purpose, the extraction rules are formulated to screen the candidate regions. The color image is fused and filtered in the neighborhood, the pixels of the image are extracted, and the 2D texture recognition is realized by multilevel fusion and visual feature reconstruction. Using compact sampling to extract more target features, feature points are matched, the coordinate system of known image information are integrated into a unified coordinate system, and design images are generated to complete art-aided design. Simulation results show that the proposed method is more accurate than the original method for extracting the information of known images, which helps to solve the problem of clearly visible output images and improves the overall design effect. [ABSTRACT FROM AUTHOR]

Graph is a data structure that can represent complex relationships among data. Graph signal processing, unlike traditional signal processing, explicitly considers the structure and relationship among the signal samples. Graph wavelet transform can provide a multiscale analysis for the graph signal. It is well known that texture is a region property in an image, which is characterized with the intensity and relationship among pixels. In this context of the graph signal processing framework, an image texture can be considered as the signal on the graph. Therefore, a texture classification method based on graph wavelet transform is proposed. Specifically, image textures are decomposed into multiscale components by using two‐channel graph wavelet filter banks. Then the local singular value decomposition is applied to each subband. In order to improve the noise‐resistant ability, the maximum, mean and median values of the local singular values of graph‐wavelet transformation coefficients are extracted. Finally, the Weibull distributions are used to model those extracted values to describe the image textures. The experiments on the benchmark texture datasets are conducted to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Global biodiversity loss is most pronounced in the tropics. Monitoring of broad-scale patterns of habitat is essential for biodiversity conservation. Image texture measures derived from satellite data are proxies for habitat heterogeneity, but have not been tested in tropical forests. Our goal was to evaluate image texture to predict tropical forest bird distributions across Thailand for different guilds. We calculated a suite of texture measures from cumulative productivity (1-km fPAR-MODIS data) for Thailand's forests, and assessed how well texture measures predicted distributions of 86 tropical forest bird species in relation to body size, and nesting guild. Finally, we compared the predictive performance of combining (a) satellite image texture measures, (b) habitat composition, and (c) habitat fragmentation. We found that texture measures predicted occurrences of tropical forest birds well (AUC = 0.801 ± 0.063). Second-order homogeneity was the most predictive texture measure. Our models based on texture were significantly better for birds with larger body size (p < 0.05), but did not differ among nesting guilds (p > 0.05). Models that combined texture with habitat composition measures (AUC = 0.928 ± 0.038) outperformed models that combined fragmentation with habitat composition measures (AUC = 0.905 ± 0.047) (p < 0.05). The incorporation of texture, composition, and fragmentation variables significantly improved model accuracy over texture-only models (AUC = 0.801 ± 0.063 to AUC = 0.938 ± 0.034; p < 0.05). We suggest that texture measures are a valuable tool to predict bird distributions at broad scales in tropical forests. • Satellite image texture can be a proxy for habitat heterogeneity. • We found that image texture predicted tropic bird distributions well. • Second-order homogeneity had the highest predictive power. • Texture predicted large birds especially well. • Predicting species distributions with image texture can support conservation. [ABSTRACT FROM AUTHOR]

Simple Summary: Detecting horse state after exercise is critical for maximizing athletic performance. The horse's response to fatigue includes exercise termination or exercise continuation at a lower intensity, which significantly limit the results achieved in races and equestrian competition. As conventional methods of detecting and quantifying exercise effort have shown some limitations, infrared thermography was proposed as a method of contactless detection of exercise effect. The promising correlation between body surface temperature and exercise-dependent blood biomarkers has been demonstrated. As the application of conventional thermography is limited by low specificity, advanced thermal image analysis was proposed here to visualize the link between blood biomarkers and texture of thermal images. Twelve horses underwent standardized exercise tests for six consecutive days, and both thermal images and blood samples were collected before and after each test. The images were analyzed using four color models (RGB, red-green-blue; YUV, brightness-UV-components; YIQ, brightness-IQ-components; HSB, hue-saturation-brightness) and eight texture-features approaches, including 88 features in total. In contrast to conventional temperature measures, as many as twelve texture features in two color models (RGB, YIQ) were linked with blood biomarker levels as part of the horse's response to exercise. As the detection of horse state after exercise is constantly developing, a link between blood biomarkers and infrared thermography (IRT) was investigated using advanced image texture analysis. The aim of the study was to determine which combinations of RGB (red-green-blue), YUI (brightness-UV-components), YIQ (brightness-IQ-components), and HSB (hue-saturation-brightness) color models, components, and texture features are related to the blood biomarkers of exercise effect. Twelve Polish warmblood horses underwent standardized exercise tests for six consecutive days. Both thermal images and blood samples were collected before and after each test. All 144 obtained IRT images were analyzed independently for 12 color components in four color models using eight texture-feature approaches, including 88 features. The similarity between blood biomarker levels and texture features was determined using linear regression models. In the horses' thoracolumbar region, 12 texture features (nine in RGB, one in YIQ, and two in HSB) were related to blood biomarkers. Variance, sum of squares, and sum of variance in the RGB were highly repeatable between image processing protocols. The combination of two approaches of image texture (histogram statistics and gray-level co-occurrence matrix) and two color models (RGB, YIQ), should be considered in the application of digital image processing of equine IRT. [ABSTRACT FROM AUTHOR]

Timely and accurate detection of crop water stress is vital for precision irrigation. Whether the accuracy of the prevailing diagnosis of crop water stress using vegetation indices (VIs) and spectral reflectance can be improved still remains to be investigated. The crop surface characteristics such as grayscale or color vary under different water stress, so in this study one more variable, image texture, was utilized together to diagnose water stress. For this end, the canopy image of winter wheat in bloom was obtained by unmanned aerial vehicle (UAV) equipped with multispectral sensor, and the effect of soil background was eliminated using vegetation index threshold method. On this basis, Grey level co-occurrence matrix (GLCM) was used to calculate the mean (MEA), variance (VAR), homogeneity (HOM), contrast (CON), dissimilarity (DIS), entropy (ENT), second moment (SEC) and correlation (COR) of the image texture under different spatial resolutions (0.008 m, 0.01 m, 0.02 m, 0.05 m, 0.1 m and 0.2 m). Next, the canopy vegetation indices were obtained by mathematical transformation of canopy reflectance, and then sensitive image texture and vegetation indices by full subset regression method. Finally, Cubist, BPNN (Back Propagation Neural Network) and ELM (Extreme Learning Machine) methods were adopted to build the estimation models of the stomatal conductance (Gs) of winter wheat (between the sensitive image texture and Gs, and between vegetation index and Gs), and the water stress map was plotted based on the optimal Gs estimation model. The result showed: (i) the image texture obtained from the high-resolution multispectral image had a high correlation with Gs, and the image texture (VAR, HOM, CON, DIS, ENT and SEC) at 550 nm had the most significant correlation; (ii) the higher the ground resolution, the higher the correlation between the Gs and the image texture, the vegetation indices, respectively. The image texture with a ground resolution of 0.008 m combined with VIs and Gs had the highest correlation, and combining image texture and vegetation index can significantly improve the estimation accuracy of winter wheat Gs; (iii) Among the three estimation models, the BPNN model constructed by combining the image texture and VIs (MEA, VAR, ENT, DWSI and EXG) had the best estimation performance (Calibration: R c 2 = 0.899, RMSE c = 0.01, MAE c = 0.006; Validation: R c 2 = 0.834, RMSE v =;0.018, MAE v = 0.014), and an accurate estimation could even be achieved at a lower Gs value. Compared with the BPNN model solely based on VIs or image texture, the R c 2 of the BPNN model based on the combined variables increased by 24% and 22.48%, respectively. Therefore, combining UAV multispectral image texture and VIs to estimate Gs provides a feasible and accurate method for water stress diagnosis of winter wheat. • The incorporation of texture improved the accuracy of water stress diagnoses with VIs. • BPNN model with VIs and texture provided the highest accuracy of Gs estimation. • UAV provides a feasible and accurate method for water stress diagnosis of winter wheat. • Removing soil background and screening methods improve the quality of remote sensing data. [ABSTRACT FROM AUTHOR]

• Propose a computer vision method for detecting asphalt pavement segregation. • Employ image texture analysis for characterizing pavement surface condition. • Extreme gradient boosting machine and deep neural network are used for classification. • Attractive repulsive center-symmetric local binary pattern is used for texture computation. • XGBoost has achieved the best detection accuracy with accuracy rate = 0.95. Aggregate segregation is a major form of defect that accelerates the pavement deterioration rate. Therefore, asphalt pavement segregation needs to be detected accurately and early during the quality survey process. This study proposes and verifies a computer vision based method for automatic identification of aggregate segregation. The new method includes Extreme Gradient Boosting Machine integrated with Attractive Repulsive Center-Symmetric Local Binary Pattern (ARCSLBP-XGBoost) and Deep Convolutional Neural Network (DCNN). Experimental results obtained from a repetitive random data sampling process with 20 runs show that the ARCSLBP-XGBoost is a capable approach for detecting asphalt pavement segregation with outstanding performance measurement metrics (classification accuracy rate = 0.95, precision = 0.93, recall = 0.98, and F1 score = 0.95). [ABSTRACT FROM AUTHOR]

The nonlinear time-series analysis method, based on the recurrence plot theory, has received great attention from researchers and has been successfully used in multiple fields. However, traditional recurrence plots that use Heaviside step functions to determine the recursive behavior of a point in the phase space have two problems: (1) Heaviside step functions produce a rigid boundary, resulting in information loss; and (2) the selection of the critical distance, ε, is crucial; if the selection is inappropriate, it will result in a low-dimensional dynamics error, and as of now, there exists no unified method for selecting this parameter. With regard to the problems described above, the novelty of this article lies in the following: (1) when determining the state-phase point recursiveness, a Gaussian function is used to replace the Heaviside function, thereby solving the rigidity and binary value problems of the recursive analysis results caused by the Heaviside step function; and (2) texture analysis is performed on a recurrence plot, new ways of studying complex system dynamics features are proposed, and a system of complex system dynamic-like measurement methods is built. [ABSTRACT FROM AUTHOR]

This paper presents an image watermarking technique for authentication and self-recovery called AuSR2. The AuSR2 scheme partitions the cover image into 3 × 3 non-overlapping blocks. The watermark data is embedded into two Least Significant Bit (LSB), consisting of two authentication bits and 16 recovery bits for each block. The texture of each block is preserved in the recovery data. Thus, each tampered pixel can be recovered independently instead of using the average block. The recovery process may introduce the tamper coincidence problem, which can be solved using image inpainting. The AuSR2 implements the LSB shifting algorithm to increase the imperceptibility by 2.8%. The experimental results confirm that the AuSR2 can accurately detect the tampering area up to 100%. The AuSR2 can recover the tampered image with a PSNR value of 38.11 dB under a 10% tampering rate. The comparative analysis proves the superiority of the AuSR2 compared to the existing schemes. [ABSTRACT FROM AUTHOR]

There are two application problems in the iris multi-category recognition scenes, namely: when adding a new template category, the expansion convenience problem caused by the difficulty of category expansion, and the environment independence demand caused by the distortion of the iris image information. To solve these two application problems, we propose an iris recognition model. The model is divided into two stages, namely: the first recognition stage and the second recognition stage. According to the orderly arrangement in the same category sample clustering range of each dimensional feature value, a 32-dimensional continuous vector space is formed as the first recognition stage feature knowledge. The 32-dimensional ordered continuous array on the basis of grayscale stable features is used as the feature knowledge in the second recognition stage. The result in the first recognition stage is divided into three types: result category, pending category, and elimination category. The second recognition stage is a specific process that is initiated when the result category is not unique or there is a pending category. Through a specially designed non-template matching function, accurate result can be obtained in the pending categories. The results of experiments with different iris libraries verify that continuous feature space based on image texture can effectively reduce the influence of image information distortion. Additionally, each feature data dimension as a training unit is conducive to the independent training of feature knowledge in single category. It can add new iris categories without interference and solve the problem of expansion convenience. [ABSTRACT FROM AUTHOR]

• The addition of texture features could improve the accuracy of soil interpretations with remote sensing. • The optimal single-scale window size of the feature parameters was extracted. • The optimal multiscale combination of window sizes for each parameter were determined. • The multiscale fusion of texture feature windows was better than the single-scale window in soil mapping. Studying the spatial distribution of soil types is an important academic and practical issue in agriculture. With the rapid development of remote sensing technology, the role of image texture as an auxiliary variable in remote sensing identification of objects has increased. It is of great importance to ascertain the optimal window size for extracting texture features and the multiscale fusion of texture feature parameters under the optimal window for different soil types. To reach this goal, soil types in a typical area of the Jiaodong Peninsula were selected as the subject investigated, homogeneity and entropy were selected as the two texture feature parameters, and the ability to identify the different soil types based on the textural features was systematically analyzed by using Landsat 8 remote sensing images. Moreover, the optimal window sizes for extracting texture features were determined, and the role of multiscale textural features in the classification of the soil types was also evaluated. The results show that the accuracy of classification significantly increased with the addition of textural features. The optimal single-scale window sizes for the homogeneity and entropy feature parameters were 19 × 19 and 21 × 21, respectively. The fusion of multiscale textural features further improved the classification accuracy. The optimal multiscale window sizes for the homogeneity were 7 × 7, 13 × 13, 19 × 19 and 21 × 21 and those for entropy were 5 × 5, 15 × 15, 21 × 21 and 23 × 23. Therefore, the method of using texture information in remote sensing images as auxiliary variables in digital soil mapping was feasible. The method of multiscale fusion of texture features, which resulted in greater classification accuracy, was better than that of single-scale window. These conclusions could play an important guiding role in soil digital mapping with remote sensing. [ABSTRACT FROM AUTHOR]

This paper uses a fuzzy set scheme to present an adaptive fractional differential algorithm for image edge enhancement and texture preservation. In the proposed algorithm, an image's membership function and area feature are used to calculate the fuzzy set of images. The function of adaptive fractional differential order (FAFDO) can be constructed by making the linear transformation of the fuzzy set. Then, the fuzzy adaptive fractional differential mask (FAFDM) is obtained by substituting the FAFDO into the fractional differential mask. Finally, the image edge and texture are enhanced and preserved by applying airspace filtering of the FAFDM convolution. The experimental results show that, compared to fractional differential or fuzzy set‐based image enhancement algorithms, the proposed algorithm can adaptively enhance the image edge and preserve the image texture by analysing the fuzziness of the image itself. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis of its severity for machine health monitoring can be stated as a nested classification problem. For a faulty bearing, each fault location whether belonging to inner race, outer race or the ball can be seen as multiclass classification with three classes while the varying degree of severity in each class can be viewed as a sub classification task. The peculiar vibration patterns generated from the flaws in different bearing parts and with varying degree of distortion can be classified into various classes and subclasses for analysis of vibration signatures. This paper proposes a multiclass support vector machines (MSVMs) based fault classification approach for fault diagnosis of ball bearings. The one dimensional vibration signals are converted to two dimensional gray scale images resulting in textural patterns which are then enhanced using the wave atom transform. Features such as semivariance, skewness and entropy are extracted from the texture images and the MSVM is then trained using feature matrices generated from feature vectors. The MSVM is trained in two phases; in the first phase, the classifier categorizes the location of the fault and in the second phase the classifier does the diagnosis regarding the size of the fault at that particular location. Simulation results show that the proposed technique is highly robust in locating the fault and its severity. [ABSTRACT FROM AUTHOR]

Image processing has played a relevant role in various industries, where the main challenge is to extract specific features from images. Specifically, texture characterizes the phenomenon of the occurrence of a pattern along the spatial distribution, taking into account the intensities of the pixels for which it has been applied in classification and segmentation tasks. Therefore, several feature extraction methods have been proposed in recent decades, but few of them rely on entropy, which is a measure of uncertainty. Moreover, entropy algorithms have been little explored in bidimensional data. Nevertheless, there is a growing interest in developing algorithms to solve current limits, since Shannon Entropy does not consider spatial information, and SampEn2D generates unreliable values in small sizes. We introduce a proposed algorithm, EspEn (Espinosa Entropy), to measure the irregularity present in two-dimensional data, where the calculation requires setting the parameters as follows: m (length of square window), r (tolerance threshold), and ρ (percentage of similarity). Three experiments were performed; the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters m, r, and ρ. Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data; the recommended parameters for better performance are m = 3, r = 20, and ρ = 0.7. [ABSTRACT FROM AUTHOR]

Recent asteroid missions have revealed that many sub-kilometer asteroids are rubble piles. Large parts of their surfaces are covered with boulders larger than tens of centimeters. An evaluation of the abundance and size distribution of boulders provides clues to understand surface processes on boulder-covered asteroids. Here we report a new method that automatically measures the abundance of small boulders (sub-pixel to a few pixels), whose boundaries cannot be recognized with visual inspection, by quantifying the surface radiance variation that occurs during the spinning of the asteroid. After validating our approach with previous boulder counting data, we apply this method to images of the asteroids Ryugu and Itokawa, which were visited by JAXA's Hayabusa and Hayabusa2, and obtain a global distribution of the boulders larger than 0.75–3 ​m, which corresponds to 1.5–6 pixels. We find that the boulder number density of this size range is smaller (1) on the western bulge than on the eastern hemisphere and (2) on the equatorial ridge than on the higher latitudes, both of which exceed the number density of boulders ​> ​5 ​m by an order of magnitude. The boulder size distribution at 1.25–20 ​m shows that the boulders smaller than 1 ​m are more abundant at the equator than at mid-latitudes, while those larger than 1 ​m in diameter are more abundant at mid-latitudes than at the equator. This contrast suggests size-dependent migration of boulders in the latitudinal direction. We also find that the typical boulder size (the size reaching the cumulative areal coverage of 50%) is 1.9 ​m at the equatorial region (10°S-10°N) while it is 2.6 ​m at mid-latitudes (40°S-50°S, 40°N-50°N). The typical boulder size is also smaller in the western bulge (2.0–2.2 ​m). We construct global maps of the power-law index of the size frequency distribution of boulders and find minor variations over the entire surface of Ryugu (−2.53 ​± ​0.03) for boulders larger than 1.25 ​m. This small variation suggests homogeneous size sorting processes on Ryugu. Surface roughness does not show a significant correlation with the v-band albedo but shows a high anti-correlation (R ​= ​−0.73) with the current geological slope on the eastern hemisphere. Our method is useful enhancement of smooth area detection and boulder distribution characterization that will be applicable to other planetary explorations in the future, including those of Phobos and other asteroids. • Automatic analysis of pixel-scale boulders based on pixel radiance variation as asteroid spins. • Distribution of sub-meter boulders on Ryugu indistinguishable from visual counting. • Method detects the east-west dichotomy in the distribution of small boulders (several meters in diameter). • Uniform power-law index of boulder size frequency distribution on Ryugu suggests homogeneous size sorting. • Boulder distribution is correlated with current geologic slope. [ABSTRACT FROM AUTHOR]

Existing texture filtering techniques acquiesce in the removal of all undesired background texture patterns simultaneously, and lead to the disappearance of small-scale foreground structures. Differently, this paper considers both multi-scale structures and specified texture patterns within an image as significant image contents, and presents a content-aware texture filtering approach via an easy-to-use interactive interface, named intuitive single click. The user's effort on content-aware texture filtering is reduced to only locating a single click, where it is intuitively considered as one type of texture patterns. This simple interface is made possible by first generating three-scale intermediate filtered images (i.e., high, medium and low) and then performing breadth-first search (BFS) to identify one or more texture regions. Specifically, we utilize BFS to purify image edges in the medium-scale filtered image, on top of which a mask representing texture and non-texture regions is abstracted through the intuitive single-click texture locating and BFS. The mask is applied to effectively maintain multi-scale structures in the low-scale filtered image, and guides to fully smooth textures in the high-scale filtered image. Finally, a clean image is yielded by simply synthesizing the results of two mask operations in the low- and high-scale filtered images. We illustrate by extensive experiments that our approach advances the state-of-the-arts in terms of both the image cleanness and content-preserving ability. Our code is publicly available. • We propose a multi-scale texture filtering approach for fully removing textures while preserving image contents effectively. • We propose a texture-aware approach for segmenting an image into contents and texture regions. • We provide an easy-to-use interactive system for allowing users to removing different types of texture patterns. [ABSTRACT FROM AUTHOR]

As one of fundamental texture classification methods, LBP-based descriptors have attracted considerable attention due to the efficiency, simplicity, and high performance. However, most of binary pattern methods cannot effectively capture the texture information with scale changes. Inspired by this, this paper proposes a multi-scale threshold integration encoding strategy for texture classification. The essence of this strategy is to introduce the multi-scale local texture information in the view of thresholding. Based on this, we propose the local multi-scale center pattern, local multi-scale sign pattern, and local multi-scale magnitude pattern to extract and describe the multi-scale local texture information. Then, the three sub-patterns are jointly combined to generate the final descriptor for texture classification tasks. The experimental results on three popular texture databases significantly demonstrate that the proposed texture descriptor is very discriminative and powerful for visual texture classification tasks. [ABSTRACT FROM AUTHOR]

Abstract This article proposes a noninvasive computer-aided assessment approach based on optimized convolutional neural network for healthy and pathological voice detection. Firstly, the input voice samples are first transformed into mel-spectrogram time-frequency visual representations and fed for training the CNN model. The time-frequency image captures inherent speech variations beneficial for healthy and pathological voice sample detection. The weights and biases of trained CNN network are further optimized using artificial bee colony (ABC) optimization algorithm resulting in optimum CNN network employed for testing unseen data. The proposed approach is evaluated using three popular and publicly available datasets: SVD, AVPD and VOICED. Experimental results emphasize that proposed ABC optimized CNN model shows improved accuracy performance by 1.02% compared to conventional CNN network illustrating data-independent discriminative representation ability. Finally, gradient-weighted class activation mapping (Grad-CAM) explainable artificial intelligence (XAI) is utilized to make the decision understandable. [ABSTRACT FROM AUTHOR]

The diversity of informal settlement morphologies across locales makes their mapping inherently challenging in heterogeneous urban landscapes. The aim of this study was to evaluate the potential of pansharpening techniques on Sentinel 2A data, and textural features, in enhancing informal settlement identification accuracy in a fragmented urban environment. Brovey transform, intensity, hue and saturation transform, Environmental Systems Research Institute (ESRI), simple mean, and Gram–Schmidt techniques were employed to pansharpen multispectral bands of Sentinel 2A, bands 5, 6, and 7 in the first group, and bands 8A, 11 and 12 in another, using an average of bands 4 and 8 as the panchromatic band. The main objective was to investigate the efficacy of pansharpening Sentinel 2A imagery and texture analysis in automated mapping of morphologically varied informal settlements. An evaluation of the quality of fused images was undertaken through computation of the correlation between the spectral values of the original multispectral and pansharpened image. Grey-level-co-occurrence matrix texture features were extracted from the pansharpened images, and subsequently incorporated in the classification process, using a support vector machine classifier. Our results confirm that the Gram–Schmidt fusion technique yielded the highest informal settlement identification accuracy (F-score 95.2%; overall accuracy 91.8%). The experimental results demonstrated the potential of pansharpening Sentinel 2A, and the added value of image texture for a more nuanced characterisation of informal settlements. [ABSTRACT FROM AUTHOR]

• Cities are dominated by private lands which presents a challenge for field study. • LiDAR data characterizes indices of avifaunal habitat on private residential lands. • NDVI, image texture, and land cover data are weaker predictors of urban avifauna. • LiDAR combined with street tree data has the strongest predictive power for urban birds. • High-resolution remote sensing data should be utilized in urban avifaunal studies. Urban ecosystems are dominated by private lands which poses a significant hurdle to performing field-based assessment of wildlife. An alternative approach is to characterize indices of animal habitat in difficult-to-access areas using data from airborne remote sensing platforms. Characterizing indices of wildlife habitat using remotely sensed data is common in natural systems but has received less attention within urban ecosystems. We tested the utility of using remotely sensed data from high-resolution airborne sensors, including LiDAR, a measure of vertical habitat structure, NDVI, a measure of greenness, image texture, a measure of horizontal habitat structure, and parcel level land-cover data, along with field-based street-tree measurements to predict bird abundance and richness across Greater Los Angeles, California, USA. We surveyed birds and gathered street-tree data on public lands of residential neighborhoods and processed the remote sensing data in 50-m and 300-m circular buffers of bird survey locations to capture data primarily on private, residential land across three winter field seasons (2016–18, 2019/20) at 23 locations along a tree-canopy cover gradient. Data from LiDAR processed as an index for the density of trees summarized in the 50-m and 300-m extents were the strongest univariate predictors of avifaunal abundance and richness explaining 75 % and 74 % of the likelihood in fitted models. NDVI, image texture, land cover, and street-tree density measures were weaker univariate predictors than models fitted with LiDAR data. Models including LiDAR and ground-based street-tree measurements accounted for upwards of 80 % of the variability in avifaunal abundance and richness, particularly for bird species associated with trees and shrubs. We recommend the prioritization of high-resolution remote sensing data, particularly LiDAR, in combination with field-based habitat measures e.g., street trees, to characterize indices of avifaunal habitat on public and private lands of cities, which could help to improve our understanding of the distribution of birds across urban areas. [ABSTRACT FROM AUTHOR]

Due to the emphasis on sustainable development and environmental protection, searching for new technologies in the production of fruit and vegetables is essential. Organic farming using natural preparations is aimed to produce healthy food, rich in nutrients that is unmodified and uncontaminated. Pepper fruit is characterized by its valuable chemical composition and dietary and taste values. The available information on the influence of natural preparations on pepper fruit is insufficient. Therefore, this study was aimed at evaluating the influence of the natural preparations and plant extracts, such as organic fertilizer - Bio-algeen S90, growth and development stimulator - Natural Crop® SL, nettle extract (fertilizer with a killing and nutritional effect) on the chemical composition, structure (image texture), and sensory quality of Devito F 1 and Sprinter F 1 pepper fruit in organic greenhouse cultivation under the conditions for organic farming. The combination of measurements of chemical properties, flesh image features, and sensory analysis for assessing the quality of pepper treated with these preparations is a great novelty of this study. The obtained results showed that the use of Bio-algeen S90 and Natural Crop® SL had an impact on the increase in the total yield compared to the control samples. Furthermore, the application of Bio-algeen S90 and Natural Crop® SL increased the content of nutrients and health-promoting compounds, such as total sugars, l -ascorbic acid, total polyphenols, and carotenoids for both pepper cultivars Devito F 1 and Sprinter F 1. The applied natural preparations had an impact on the flesh structure, which was greater for pepper Devito F 1. The selected image textures allowed for distinguishing pepper slices in terms of natural preparations with higher accuracies for Devito F 1 than Sprinter F 1. However, in the case of both cultivars, the samples treated with NaturalCrop® SL were the most different from the control. Natural preparations were not affecting significantly the sensory attributes of pepper fruit. All treated samples were characterized by high sensory quality. • The influence of the Bio-algeen S90, Natural Crop® SL, and nettle extract on pepper was examined. • The chemical properties, image parameters, and sensory attributes were determined. • The highest effect of NaturalCrop® SL on pepper fruit was confirmed. [ABSTRACT FROM AUTHOR]