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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]

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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]

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]

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]

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]

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]

Texture classification is one of the basic images processing tasks. In this paper we present some numerical characteristics to the images analysis and processing. It can be used at the solving of images classification problems, their recognition, problems of remote sounding, biomedical images analysis, geological researches.

Pronounced spin phase artifacts appear in diffusion-weighted imaging (DWI) with only minor subject motion. While DWI data corruption is often identified as signal drop out in diffusion-weighted (DW) magnitude images, DW phase images may have higher sensitivity for detecting subtle subject motion. This article describes a novel method to return a metric of subject motion, computed using an image texture analysis of the DW phase image. This Phase Image Texture Analysis for Motion Detection in dMRI (PITA-MDD) method is computationally fast and reliably detects subject motion from diffusion-weighted images. A threshold of the motion metric was identified to remove motion-corrupted slices, and the effect of removing corrupted slices was assessed on the reconstructed FA maps and fiber tracts. Using a motion-metric threshold to remove the motion-corrupted slices results in superior fiber tracts and fractional anisotropy maps. When further compared to a state-of-the-art magnitude-based motion correction method, PITA-MDD was able to detect comparable corrupted slices in a more computationally efficient manner. In this study, we evaluated the use of DW phase images to detect motion corruption. The proposed method can be a robust and fast alternative for automatic motion detection in the brain with multiple applications to inform prospective motion correction or as real-time feedback for data quality control during scanning, as well as after data is already acquired. [ABSTRACT FROM AUTHOR]

The paper proposes an algorithm for the script recognition based on the texture characteristics. The image texture is achieved by coding each letter with the equivalent script type (number code) according to its position in the text line. Each code is transformed into equivalent grey level pixel creating an 1-D image. Then, the image texture is subject to the run-length analysis. This analysis extracts the run-length features, which are classified to make a distinction between the scripts under consideration. In the experiment, a custom oriented database is subject to the proposed algorithm. The database consists of some text documents written in Cyrillic, Latin and Glagolitic scripts. Furthermore, it is divided into training and test parts. The results of the experiment show that 3 out of 5 run-length features can be used for effective differentiation between the analysed South Slavic scripts. [ABSTRACT FROM AUTHOR]

Up-to-date information regarding impervious surface is valuable for urban planning and management. The objective of this study is to develop neural computing models used for automatic impervious surface area detection at a regional scale. To achieve this task, advanced optimizers of adaptive moment estimation (Adam), a variation of Adam called Adamax, Nesterov-accelerated adaptive moment estimation (Nadam), Adam with decoupled weight decay (AdamW), and a new exponential moving average variant (AMSGrad) are used to train the artificial neural network models employed for impervious surface detection. These advanced optimizers are benchmarked with the conventional gradient descent with momentum (GDM). Remotely sensed images collected from Sentinel-2 satellite for the study area of Da Nang city (Vietnam) are used to construct and verify the proposed approach. Moreover, texture descriptors including statistical measurements of color channels and binary gradient contour are employed to extract useful features for the neural computing model-based pattern recognition. Experimental result supported by statistical test points out that the Nadam optimizer-based neural computing model has achieved the most desired predictive accuracy for the data collected in the studied region with classification accuracy rate of 97.331%, precision = 0.961, recall = 0.984, negative predictive value = 0.985, and F1 score = 0.972. Therefore, the model developed in this study can be a helpful tool for decision-makers in the task of urban land-use planning and management. [ABSTRACT FROM AUTHOR]

The cultivar and fertilization can affect the physicochemical properties of pepper fruit. This study aimed at estimating the content of α-carotene, β-carotene, total carotenoids, and the total sugars of unfertilized pepper and samples treated with natural fertilizers based on texture parameters determined using image analysis. Pearson's correlation coefficients, scatter plots, regression equations, and coefficients of determination were determined. For red pepper Sprinter F1, the correlation coefficient (R) reached 0.9999 for a texture from color channel B and −0.9999 for a texture from channel Y for the content of α-carotene, −0.9998 (channel a) for β-carotene, 0.9999 (channel a) and −0.9999 (channel L) for total carotenoids, as well as 0.9998 (channel R) and −0.9998 (channel a) for total sugars. The image textures of yellow pepper Devito F1 were correlated with the content of total carotenoids and total sugars with the correlation coefficient reaching −0.9993 (channel b) and 0.9999 (channel Y), respectively. The coefficient of determination (R2) of up to 0.9999 for α-carotene content and the texture from color channel Y for pepper Sprinter F1 and 0.9998 for total sugars and the texture from color channel Y for pepper Devito F1 were found. Furthermore, very high coefficients of correlation and determination, as well as successful regression equations regardless of the cultivar were determined. [ABSTRACT FROM AUTHOR]

In this study, the application of a backscattering imaging system with different approaches of transform-based image texture analysis for the evaluation of banana quality at different ripening stages was investigated with Wavelet, Gabor and Tamura transforms. The attenuated images of the fruits were acquired using Laser Light Backscattering Imaging (LLBI) with laser diodes emitting light at three wavelengths viz 532, 660, and 830 nm. The elasticity, chlorophyll index and soluble solids content (SSC) of each sample were measured as reference parameters by using a texture analyser, a Delta Absorbance (DA) meter, and a refractometer, respectively. The performance of the extracted features from the selected transform-based image texture analysis for analysing the quality parameters of the fruit was evaluated by means of an artificial neural network (ANN) and a support vector machine (SVM). The results indicated that there were significant changes of elasticity, chlorophyll index and SSC as the ripening stages increased. Prediction model analysis showed that the Wavelet transform exhibited the most reliable results for all of the reference parameters followed by Tamura and the Gabor transform. The results also revealed that analysis using an ANN approach recorded better performance than SVM as reflected by higher coefficient of determination (R 2 ) values. Thus, this study indicated that an LLBI system with transform-based image texture analysis coupled with computational intelligence techniques can be used for the evaluation of the quality of bananas. [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]

Image analysis gives us a new opportunity in corrosion science. Fuzzy Kolmogorov-Sinai (K-S) entropy is used to quantify the average amount of uncertainty of a dynamical system through a sequence of observations. The fuzzy K-S entropy for horizontal and vertical orientations is sensitive to distribution of corrosion product or corrosion degree, and the entropy values decrease as the corrosion becomes more and more serious. It is concluded that the fuzzy K-S entropy is illustrated as an effective feature for image analysis and corrosion classification. [ABSTRACT FROM AUTHOR]

This paper uses partial differential equation image processing techniques to establish image texture analysis models based on nonlinear anisotropic diffusion equations for image denoising, image segmentation, and image decomposition. This paper proposes a class of denoising models based on the hybrid anisotropic diffusion equation from the characteristics of different noise types. The model exhibits anisotropic diffusion near the image boundary, which can protect the boundary well, and isotropic diffusion inside the image; so, it can remove the noise effectively. We use the immovable point theory to prove the uniqueness of the model solution and further discuss other properties such as asymptotics of the solution. We propose a class of image texture analysis algorithms based on anisotropic diffusion equations and discrete gray level sets. First, a class of nonconvex generalized functions is proposed to remove the noise from the original image to obtain a smooth image while sharpening the edges. Then, an energy generalization function based on the gray level set is proposed, and the existence of the global minimum of this energy generalization function is discussed. Finally, an equivalent form of this energy generalization is given in the discrete case, and an image texture analysis algorithm is designed based on the equivalent form. The algorithm is improved by initial position optimization, dynamic adjustment of parameters, and adaptive selection of thresholds so that the ants can search along the real edges. Experiments show that the improved algorithm for image edge detection can obtain more complete edges and better detection results. The energy generalization function is calculated directly on the discrete gray level set instead of solving the corresponding partial differential equation, which can avoid the selection of the initial level set and the reinitialization of the level set, thus greatly improving the segmentation efficiency. The new algorithm has a high improvement in segmentation efficiency and can efficiently handle large size complex images. [ABSTRACT FROM AUTHOR]

Reports have suggested that tumor textures presented on T2-weighted images correlate with the genetic status of glioma. Therefore, development of an image analyzing framework that is capable of objective and high throughput image texture analysis for large scale image data collection is needed. The current study aimed to address the development of such a framework by introducing two novel parameters for image textures on T2-weighted images, i.e., Shannon entropy and Prewitt filtering. Twenty-two WHO grade 2 and 28 grade 3 glioma patients were collected whose pre-surgical MRI and IDH1 mutation status were available. Heterogeneous lesions showed statistically higher Shannon entropy than homogenous lesions (p = 0.006) and ROC curve analysis proved that Shannon entropy on T2WI was a reliable indicator for discrimination of homogenous and heterogeneous lesions (p = 0.015, AUC = 0.73). Lesions with well-defined borders exhibited statistically higher Edge mean and Edge median values using Prewitt filtering than those with vague lesion borders (p = 0.0003 and p = 0.0005 respectively). ROC curve analysis also proved that both Edge mean and median values were promising indicators for discrimination of lesions with vague and well defined borders and both Edge mean and median values performed in a comparable manner (p = 0.0002, AUC = 0.81 and p < 0.0001, AUC = 0.83, respectively). Finally, IDH1 wild type gliomas showed statistically lower Shannon entropy on T2WI than IDH1 mutated gliomas (p = 0.007) but no difference was observed between IDH1 wild type and mutated gliomas in Edge median values using Prewitt filtering. The current study introduced two image metrics that reflect lesion texture described on T2WI. These two metrics were validated by readings of a neuro-radiologist who was blinded to the results. This observation will facilitate further use of this technique in future large scale image analysis of glioma. [ABSTRACT FROM AUTHOR]

Featured Application: This research applied the described methods of image analysis to create a robust analysis model to develop multiple avenues of research of great interest in biomedicine. Low back pain is the leading cause of disability in the world, with a significant socio-economic impact. Deep dry needling is effective in the treatment of this pain, and it is one of the techniques preferred by physiotherapists. In this field, the use of ultrasound provides information of interest such as length, thickness, diameter, cross-sectional area, or muscle volume, among others. Objective: To find out whether the tissue changes (thickness, histogram, and contraction rate) that occur in the lumbar multifidus after application of the deep dry needle are related to changes in the pain and the disability of the patient. Design: Randomized, double-blind, parallel-group clinical trial. Setting: University of Alcalá, Department of Physiotherapy. Subjects: 21 voluntary patients (women and men) with non-specific low-back pain aged 18–65 years. Intervention: Patients were randomly divided into two groups. One group received dry needling and the other group a dry needling placebo. Initial post-needling and one week post-needling assessments were performed by a therapist blinded to the intervention. Variables: Lumbar multifidus thickness measured by RUSI, contraction time measured by M-mode, histograms measured by image analysis, muscle area, pain measured by VAS, pressure pain threshold measured by pressure algometer, and disability measured by Roland–Morris questionnaire. Conclusions: The contraction speed, resting thickness, and pain demonstrated significant differences within each group, but not between groups. There were significant differences in contraction ratio and in PPT between groups. There was excellent intra-examiner reliability in image collection for histogram analysis. Histogram analysis showed no significant differences between groups and measurements, neither for the parameters nor for the parameters combined with the outcome variables. A robust method for the image texture analyses in future histogram muscle analyses has been performed. [ABSTRACT FROM AUTHOR]

The recognition of wood species is a laborious process, which is performed by experts, who attempt to distinguish the different species in different wood sections based on their macroscopic and microscopic characteristics. Most of these characteristics can be observed in the transverse or cross section of woods. According to experts, the next most important surface for wood species recognition is the tangential section while significant information can also be obtained from the radial section of woods. Based on the recent advances in the area of computer vision and pattern recognition, most researchers have proposed imagebased approaches attempting to address the problem either in microscopic or macroscopic scale. The main limitation is that in many cases there are some features that are not visible in each wood section. Firstly, we examine the contribution of each section in wood species recognition using two different computer-based texture analysis methods. Furthermore, we compare wood species recognition methods for both grayscale images and colorscale images. Finally, we propose a novel fusion method and we demonstrate that wood species recognition accuracy can be increased by fusing features from different wood sections. For the evaluation of the proposed method, a dataset, namely "WOOD-AUTH", consisting of more than 4272 wood images of twelve common wood species, was used. [ABSTRACT FROM AUTHOR]

Semi-variogram estimators and distortion measures of signal spectra are utilized in this paper for image texture retrieval. On the use of the complete Brodatz database, most high retrieval rates are reportedly based on multiple features and the combinations of multiple algorithms, while the classification using single features is still a challenge to the retrieval of diverse texture images. The semi-variogram, which is theoretically sound and the cornerstone of spatial statistics, has the characteristics shared between true randomness and complete determinism and, therefore, can be used as a useful tool for both the structural and statistical analysis of texture images. Meanwhile, spectral distortion measures derived from the theory of linear predictive coding provide a rigorously mathematical model for signal-based similarity matching and have been proven useful for many practical pattern classification systems. Experimental results obtained from testing the proposed approach using the complete Brodatz database, and the the University of Illinois at Urbana-Champaign texture database suggests the effectiveness of the proposed approach as a single-feature-based dissimilarity measure for real-time texture retrieval. [ABSTRACT FROM PUBLISHER]

Abstract: This paper presents a machine vision system for the automatic characterization of the quality properties of nanofiber assemblies using image texture analysis techniques. The objective is to use a digital image of a nanofiber membrane in order to estimate the pore size distribution (PSD), fiber diameter distribution (FDD) and permeability of the assembly. The underlying idea is that, after textural features have been extracted from the image in the form of statistical descriptors, these descriptors can be related to the membrane properties using multivariate regression techniques. Two alternative feature extraction techniques are considered, a statistical-based approach (namely, gray-level co-occurrence matrix, GLCM) and a transform-based approach (based on the use of wavelet transforms). Polymer nanofiber membranes fabricated by electrospinning are used as test beds of the proposed system. The estimation results are very satisfactory for all properties with any of the two feature extraction techniques, with the wavelet transform-based approach slightly outperforming the GLCM one. Whereas for the estimation of the PSD and FDD a scanning electron microscope image of the product needs to be available, it is shown that as far as permeability is concerned an image at a much lower magnification scale (i.e. an optical microscope one) is sufficient to provide an accurate property estimation. Based on these results, the proposed system represents a very promising step toward the complete automation of quality assessment procedures in nanomaterials processing. [Copyright &y& Elsevier]

We propose a texture insensitive, region based image saliency detection algorithm, having excellent detection and localization properties, to obtain salient objects. We use a total variation based regularizer to suppress textures from the image and to make the method invariant to textural variations in the scene. This leads to an image that contains piecewise constant gray valued regions. This texture-free image is sparsely segmented into a small number of regions using the expectation maximization algorithm assuming a Gaussian mixture model. We compute three different saliency measures for every region using its intensity and spatial features. We adopt a relevance feedback mechanism to obtain weights for combining the three saliency measures and obtain the final saliency map. Next we input the thresholded saliency map to an image matting technique and extract the salient objects from the image with exact boundaries. Experimental comparisons with existing saliency detection algorithms demonstrate the superiority of the proposed technique. [ABSTRACT FROM AUTHOR]

The existing methods -- such as sieving, microscope, light scattering, sedimentation, and electrical induction for pigment size detection -- require sampling or scattering the mineral pigments, which will inevitably cause damage to the films painted by mineral pigments. A new detection method based on run length texture analysis is proposed to nondestructively detect the pigment size in the mineral paint film. The films painted by mineral pigments with preknown pigment sizes are contactlessly captured by CCD microscope under diffused light. Gray transform, histogram equalization, and median filtering are implemented to preprocess the captured images, and then the run length texture parameters are extracted from the preprocessed images. A parametric relationship between the extracted parameters and the preknown size is established to predict the pigment size in mineral paint film nondestructively. Burnt carnelian is selected as the sample to verify the feasibility of the proposed method. Results show that the max detection error of the proposed method is 5.548 μm and can be applied to the size detection of the mineral pigments used in mineral paint film. [ABSTRACT FROM AUTHOR]

The article presents a study that examined the importance of magnetic resonance (MR) image texture analysis method in diagnosing patients with Parkinson's disease (PD), a degenerative disorder of the central nervous system. The researchers found the potential of the method to detect brain structural changes and suggested the enhancement of the method's protocol and repeatability before possible clinical use. Details on the materials and methods used by the researchers are discussed.

The Poisson equation is a class of partial differential equations which describe a steady-state temperature distribution in a bounded object. This paper applies this equation to the modelling of image textures by constructing specific heat source functions and boundary conditions. The heat source function can be considered as an image transform function such that a set of texture features at different frequencies and orientations can be extracted from the transformed image, in conjunction with using a Gabor wavelet filer bank. Better performance of image texture retrieval by these features is achieved than using the features extracted directly from the original image texture. [Copyright &y& Elsevier]

• 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]

Being one of the most important attributes that affect the eating quality of meat products, tenderness is still mostly evaluated using sensory panel and instrumental methods. It is desirable to develop a fast, non-destructive, accurate, and on-line technique for tenderness evaluation of meat products. As an objective, consistent, rapid, and automatic technique, computer vision could be employed to complete such a task. The relationships between tenderness and image texture features of pork ham were investigated in this study. Fifty observations were made, and shear force was measured as the indicator of tenderness. Five approaches were employed to characterize the image texture features of pork ham, including the common first-order gray-level statistics (FGLS), run length matrix (RLM), gray-level co-occurrence matrix (GLCM), fractal dimension (FD), and wavelet transform (WT) based method. After that, both simple correlation analysis and partial least squares regression (PLSR) analysis were carried out to study the relationships between the tenderness of pork ham and the extracted image texture features. It was found that the image texture features extracted using the WT-based method had the best relationships with the tenderness of pork ham. However, there were no significant correlations found between the tenderness of pork ham and the image texture features extracted by the traditional methods, including FGLS, RLM, and GLCM (P > 0.05). [ABSTRACT FROM AUTHOR]

The degree of surface roughness as measured using Fractal Dimension and Lacunarity can serve as quality measures of the coating process. The smoothness of the surface before and after coating is an important characteristic frequently used in surface engineering, to obtain well-polished and smoothened surface finish. We have used Brass plates and coated it with Nickel using electroless and electroplating methods. The images of the coated surfaces were taken using an optical microscope before and after coating. Fractal Dimension (FD) and Lacunarity (λ) of the surface image before and after coating is calculated for comparison using Differential Box-Counting algorithm. Electro-cleaning of the samples in an alkaline degreasing solution is done. Electroplating is carried out in respective electrolytic bath under constant current and temperature conditions. Electroless coating is carried out with a separate Nickel bath at constant temperature of 85° C. The experimental data was validated, which revealed that FD and λ values decreased after coating in all the cases. The lower value of FD and λ after coating suggests more regularity in the surface patterns or smoothness. Hence, we conclude that FD and λ serve as relevant parameters depicting the quality of coating surface at significance level of 0.05. [ABSTRACT FROM AUTHOR]

The different cultivars of apricot seeds may differ in their properties. To ensure economical and efficient seed processing, knowledge of the cultivars' composition and physical properties may be necessary. Therefore, the correct identification of the cultivar of the apricot seeds may be very important. The objective of this study was to develop models based on selected textures of apricot seed images to distinguish different cultivars. The images of four cultivars of apricot seeds were acquired using a flatbed scanner. For each seed, approximately 1600 textures from the image, converted to the different color channels R, G, B, L, a, b, X, Y, and Z, were calculated. The models were built separately for the individual color channels; the color spaces Lab, RGB, XYZ; and all color channels combined based on selected texture parameters using different classifiers. The average accuracy of the classification of apricot seeds reached 99% (with an accuracy of 100% for the seeds of the cultivars 'Early Orange', 'Bella', and 'Harcot', and 96% for 'Taja') in the case of the set of textures selected from the color space Lab for the model built using the Multilayer Perceptron classifier. The same classifier produced high average accuracies for the color spaces RGB (90%) and XYZ (86%). For the set of textures selected from all color channels, i.e., R, G, B, L, a, b, X, Y, and Z, the average accuracy reached 96% (Multilayer Perceptron and Random Forest classifiers). In the case of individual color channels, the highest average accuracy was up to 91% for the models built based on a set of textures selected from color channel b (Multilayer Perceptron). The results proved the possibility of distinguishing apricot seed cultivars with a high probability using a non-destructive, inexpensive, and objective procedure involving image analysis. [ABSTRACT FROM AUTHOR]

During the phase of building survey, spalling and its severity should be detected as earlier as possible to provide timely information on structural heath to building maintenance agency. Correct detection of spall severity can significantly help decision makers develop effective maintenance schedule and prioritize their financial resources better. This study aims at developing a computer vision-based method for automatic classification of concrete spalling severity. Based on input image of concrete surface, the method is capable of distinguishing between a minor spalling in which the depth of the broken-off material is less than the concrete cover layer and a deep spalling in which the reinforcing steel bars have been revealed. To characterize concrete surface condition, image texture descriptors of statistical measurement of color channels, gray-level run length, and center-symmetric local binary pattern are used. Based on these texture-based features, the support vector machine classifier optimized by the jellyfish search metaheuristic is put forward to construct a decision boundary that partitions the input data into two classes of shallow spalling and deep spalling. A dataset consisting of 300 image samples has been collected to train and verify the proposed computer vision method. Experimental results supported by the Wilcoxon signed-rank test point out that the newly developed method is highly suitable for concrete spall severity classification with accuracy rate = 93.33%, F1 score = 0.93, and area under the receiver operating characteristic curve = 0.97. [ABSTRACT FROM AUTHOR]

To inspect the quality of concrete structures, surface voids or bugholes existing on a concrete surface after the casting process needs to be detected. To improve the productivity of the inspection work, this study develops a hybrid intelligence approach that combines image texture analysis, machine learning, and metaheuristic optimization. Image texture computations employ the Gabor filter and gray-level run lengths to characterize the condition of a concrete surface. Based on features of image texture, Support Vector Machines (SVM) establish a decision boundary that separates collected image samples into two categories of no surface void (negative class) and surface void (positive class). Furthermore, to assist the SVM model training phase, the state-of-the-art history-based adaptive differential evolution with linear population size reduction (L-SHADE) is utilized. The hybrid intelligence approach, named as L-SHADE-SVM-SVD, has been developed and complied in Visual C#.NET framework. Experiments with 1000 image samples show that the L-SHADE-SVM-SVD can obtain a high prediction accuracy of roughly 93%. Therefore, the newly developed model can be a promising alternative for construction inspectors in concrete quality assessment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new algorithm for remotely sensed image texture classification and segmentation. We observe that the traditional method least square error (LSE) is unstable in practical applications. This motivates us to develop a more stable method. We have proposed the regularization technique to suppress the instability of LSE in previous research. Our contribution in this paper is that we propose a new stable method, which is based on the total variation (TV) for reducing instability in texture analysis, and apply it to remotely sensed image texture classification and segmentation. Experimental results on remotely sensed images demonstrate that our new algorithm is superior to LSE and seems promising in applications. [ABSTRACT FROM AUTHOR]

Recognition of spalling on surface of concrete wall is crucial in building condition survey. Early detection of this form of defect can help to develop cost-effective rehabilitation methods for maintenance agencies. This study develops a method for automatic detection of spalled areas. The proposed approach includes image texture computation for image feature extraction and a piecewise linear stochastic gradient descent logistic regression (PL-SGDLR) used for pattern recognition. Image texture obtained from statistical properties of color channels, gray-level cooccurrence matrix, and gray-level run lengths is used as features to characterize surface condition of concrete wall. Based on these extracted features, PL-SGDLR is employed to categorize image samples into two classes of "nonspall" (negative class) and "spall" (positive class). Notably, PL-SGDLR is an extension of the standard logistic regression within which a linear decision surface is replaced by a piecewise linear one. This improvement can enhance the capability of logistic regression in dealing with spall detection as a complex pattern classification problem. Experiments with 1240 collected image samples show that PL-SGDLR can help to deliver a good detection accuracy (classification accuracy rate = 90.24%). To ease the model implementation, the PL-SGDLR program has been developed and compiled in MATLAB and Visual C# .NET. Thus, the proposed PL-SGDLR can be an effective tool for maintenance agencies during periodic survey of buildings. [ABSTRACT FROM AUTHOR]

Abstract: Texture analyses methods incorporating three-dimensional fractal analysis using box-counting, grey level co-occurrence matrix (GLCM) technique and run length statistical (RLS) analysis have been carried out on tensile fractographs of AISI 304LN austenitic stainless steel for automatic characterization of fracture surfaces. The tensile tests have been carried out at five different strain rates (0.0001, 0.001, 0.01, 0.1 and 1s−1). The three above mentioned methods, namely, fractal analysis using box-counting, GLCM and RLS analysis are compared in terms of accuracy and computational time and amongst them the run length analysis shows the best result. Eight texture descriptors from the three texture analyses could be extracted to correlate with the observed mechanical properties. Long run emphasis (LRE) and long run high grey level emphasis (LRHGE) depict better correlation among the eight descriptors in this investigation. The results also reveal systematic variation of image texture properties with strain rate. [Copyright &y& Elsevier]

The suitability of optical IKONOS satellite data (multispectral and panchromatic) for the estimation of forest structural attributes – for example, stems per hectare (SPHA), diameter at breast height (DBH), mean tree height (MTH), basal area (BA) and volume in plantation forest environments – was assessed in this study. The relationships of these forest structural attributes to statistical image texture from IKONOS imagery were analysed. The coefficients of determination (R 2) of multilinear regression models developed for the estimation of SPHA, DBH, MTH, BA and volume using statistical texture features from multispectral data were 0.63, 0.68, 0.81, 0.86 and 0.86, respectively. When the statistical texture features from panchromatic data were applied, the R 2 for the respective forest structural attributes increased by 25%, 31%, 6%, 0.2% and 0.2%, respectively. Artificial neural network (ANN) models produced strong and significant relationships between estimated and actual measures of SPHA, DBH, MTH, BA and volume with an R 2 of 0.83, 0.83, 0.90, 0.90 and 0.92, respectively, based on multispectral IKONOS data. Based on panchromatic IKONOS imagery, the R 2 for the respective forest structural attributes increased by 18%, 12%, 5%, 3% and 6%, respectively. Results such as these bode well for the application of high spatial resolution imagery to forest structural assessment. [ABSTRACT FROM AUTHOR]

Abstract: MaZda, a software package for 2D and 3D image texture analysis is presented. It provides a complete path for quantitative analysis of image textures, including computation of texture features, procedures for feature selection and extraction, algorithms for data classification, various data visualization and image segmentation tools. Initially, MaZda was aimed at analysis of magnetic resonance image textures. However, it revealed its effectiveness in analysis of other types of textured images, including X-ray and camera images. The software was utilized by numerous researchers in diverse applications. It was proven to be an efficient and reliable tool for quantitative image analysis, even in more accurate and objective medical diagnosis. MaZda was also successfully used in food industry to assess food product quality. MaZda can be downloaded for public use from the Institute of Electronics, Technical University of Lodz webpage. [Copyright &y& Elsevier]

Abstract: The objective of this work was to assess the crumb grain appearance of bread loaves prepared with four organic flours (11.3–12.4% protein content) in comparison with a control bread prepared with non-organic flour (12.4% protein content), by means of three statistical and one spectral grey-level image texture analysis methods. Stepwise-selected textural features independently extracted from each method could successfully distinguish 94–97% of the non-organic bread’s crumb grain images, and could classify 76–83% of the organic bread’s crumb grain images into their respective flour types using cross-validation. The statistical texture analysis methods (explained variance 91–96%) depicted the bread crumb visual texture better than the spectral method (explained variance 80%). Factor analysis performed over the highest discriminant textural features identified three major components: grain coarseness, heterogeneity and isotropy, being the last component extracted only from the spectral method. This model showed that crumb grain of organic breads was variably heterogeneous, coarse and, in some cases, with a high degree of isotropy, since their cells were much more rounded than the non-organic bread’s crumb cells. [Copyright &y& Elsevier]

Surface texture is an important quality characteristic of many products. This paper provides an overview of several different approaches to image texture analysis and demonstrates their use on the problem of classifying a set of rolled steel sheets into various quality grades. Methods covered include traditional statistical approaches such as gray level co-occurrence matrix (GLCM) methods, multivariate statistical approaches based on PCA and PLS, and wavelet texture analysis.Traditional multivariate classification approaches, such as PLS-DA, applied directly to the images are shown to fail because of the loss of spatial identity of the variables (pixels) in those approaches, and the lack of congruency of the images. However, approaches that re-introduce spatial information, such as performing two-dimensional FFT on the images prior to applying multivariate methods can perform well. A new approach that re-introduces spatial information through image shifting and stacking, followed by multivariate image analysis (MIA) is presented and shown to work well. It can also be used to develop optimal spatial filters for extracting texture information. Wavelet texture analysis (WTA) methods are discussed and insight into their space/frequency decomposition behavior is used to show why they are generally considered to be state of the art in texture analysis. [Copyright &y& Elsevier]

Abstract: This paper discusses the role of illumination in discrimination of tea samples based upon textural features of tea granules. The images of tea granules were acquired using 3CCD color camera under Dual Ring light which consists of both Darkfield as well as Brightfield type of illumination. Ten graded tea samples were analyzed. Five textural features were ‘entropy’, ‘contrast’, ‘homogeneity’, ‘correlation’ and ‘energy’ obtained under both illuminations. The acquired textural features were subjected to principal component analysis (PCA). The results showed that best discrimination was obtained with Darkfield illumination with a variance of 96% whereas Brightfield illumination showed low discrimination with only 83% variance. Analysis of PCA biplot indicated correlations among graded tea samples and textural features. The study concludes that textural features may be used to estimate tea quality under Darkfield illumination being non-destructive and quick technique. [Copyright &y& Elsevier]

Abstract: Motivation: Physical properties of coal such as particle size distribution have a large influence on the stability and operational behavior of fluidized bed reactors and metallurgical furnaces. In particular, the presence of large amounts of “fine” particles invariably has a drastic effect on plant performance as a result of impaired gas permeability characteristics of the coal or ore burden. Therefore, monitoring and control of particle size distribution profiles of such aggregate material on reactor feed streams, such as moving conveyor belts, is critical for predictable operation of these processes. Traditionally, the method of sieve analysis using stock or belt cut samples has been widely used in industry. Unfortunately, the reliability and usefulness of belt cut techniques are constrained by frequency of sampling as well as laboratory analysis turnaround times. For real-time monitoring and control purposes, automated sampling and analysis methods are more desirable. Methods: In this study, the problem of estimating the particle size distribution profile of material on a moving conveyor belt is formulated within a texture classification framework, which has its basis in machine vision and incorporates elements from statistical pattern recognition. Using exemplar images of coal particles taken on a process stream, a set of local features that compactly describes the textural properties of each image are expressed in terms of localized nonlinear features called textons. Representation of image information using textons is primarily motivated by insights from neuroscience research on the optimality of linear oriented basis functions as models of perception in early processing of visual information in the cortex regions of the human brain. Using these representations for different textures, nearest neighbor and support vector machine classification models are subsequently used to classify test images. Results: Using a comprehensive evaluation, it is shown that the use of texton representation obtained from decomposing images with linear oriented basis functions can be sufficiently discriminative compared to the use of the widely used second-order statistical features or features from other baseline models. In particular, model performance obtained with appropriately tuned filters suggest the importance of including shape and spatial structure information in an image representation for texture classification of coal particles. Furthermore, using nonlinear support vector machines rather than nearest neighbor classifiers significantly improved classification performance. A texture classification approach to particle size profile estimation has potential applications in the online monitoring of the proportion of “fines” in coal material on moving conveyor belts. [Copyright &y& Elsevier]