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Solanum mauritianum is a highly destructive and resourceful plant invader, resulting in severe economic and ecological damage. Detecting and mapping the spatial distribution of S. mauritianum is a priority for effective management of commercial forest plantations. Therefore, image texture computed from a 2 m WorldView-2 image with sparse partial least squares discriminant analysis (SPLS-DA) and partial least squares discriminant analysis (PLS-DA) were developed and applied to detect and map co-occurring S. mauritianum within a commercial forest plantation. The results indicated that SPLS-DA successfully performed simultaneous variable selection and dimension reduction to yield an overall classification accuracy of 77%. In contrast, the PLS-DA model in conjunction with variable importance in the projection (VIP) yielded an overall classification accuracy of 67%. The most significant texture parameters selected by the SPLS-DA model were correlation, homogeneity and second moment, which were predominantly computed from the red, red edge and NIR bands. Overall, this study validates the potential of image texture integrated with SPLS-DA to effectively detect and map co-occurring S. mauritianum in a commercial forest plantation. [ABSTRACT FROM AUTHOR]

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]

• A new feature extraction method based on Discrete Modal Decomposition is proposed. • Modal features can firstly be computed as the DMD coordinates of the image. • Modal features can secondly be computed using the DMD as a local transform process. • Experimental tests show the relevance of the method on a texture classification task, with lower extraction times than state-of-the art methods. Feature extraction is a key step in image processing for pattern recognition and machine learning processes. Its purpose lies in reducing the dimensionality of the input data through the computing of features which accurately describe the original information. In this article, a new feature extraction method based on Discrete Modal Decomposition (DMD) is introduced, to extend the group of space and frequency based features. These new features are called modal features. Initially aiming to decompose a signal into a modal basis built from a vibration mechanics problem, the DMD projection is applied to images in order to extract modal features with two approaches. The first one, called full scale DMD, consists in exploiting directly the decomposition resulting coordinates as features. The second one, called filtering DMD, consists in using the DMD modes as filters to obtain features through a local transformation process. Experiments are performed on image texture classification tasks including several widely used data bases, compared to several classic feature extraction methods. We show that the DMD approach achieves good classification performances, comparable to the state of the art techniques, with a lower extraction time. [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]

Surface texture of an image plays an imperative role in understanding objects such as aggregates, woods, grains, soils, trees, and houses. Fractal dimension (FD) helps to characterise these objects by quantifying their complex texture patterns. Differential box counting (DBC) is one of the popularly used methods to measure FD of a gray-scale image. However, it suffers from several limitations. So, this study introduces three improved DBC methods using three box heights based on eigenvalue, kurtosis, and skewness of an image respectively. These methods also use a new xy -plane shifting mechanism and a modified formula for computing n r. Moreover, weighted least squares (WLS) regression is adopted, where a trapezoidal membership function (TMF) based rule is proposed for assigning weights to each grid size. The image surface exhibits multifractal nature, hence two multifractal analysis methods are also analysed and included in the experiments. All the experiments are performed on three gray-scale image databases viz., the synthesized Fractal Brownian motion (FBM) images, natural texture images of Brodatz database, and images of Outex database that are captured at different rotations. Experimental results show that the proposed methods outperform some of the state-of-the-art methods. Unlabelled Image • Surface texture analysis of an image is done based on fractal dimension (FD). • FD is computed using an improved differential box counting method. • This work addresses the over-counting problem along z-direction. • The under-counting problem along xy-directionis is also solved using a xy-plane shiftiting mechanism. • Weighted least squares regression is utilized to obtain a better fitted line. [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]

Global food production in the developing world occurs within sub-hectare fields that are difficult to identify with moderate resolution satellite imagery. Knowledge about the distribution of these fields is critical in food security programs. We developed a semi-automated image segmentation approach using wall-to-wall sub-meter imagery with high-performance computing to map crop area (CA) throughout Tigray, Ethiopia that encompasses over 41,000 km 2 . Multiple processing streams were tested to minimize mapping error while applying five unique smoothing kernels to capture differences in land surface texture associated to CA. Typically, very-small fields (mean < 2 ha) have a smooth image roughness compared to natural scrub/shrub woody vegetation at the ~1 m scale and these features can be segmented in panchromatic imagery with multi-level histogram thresholding. Multi-temporal very-high resolution (VHR) panchromatic imagery with multi-spectral VHR are sufficient in extracting critical CA information needed in food security programs. A 2011 to 2015 CA map was produced, using over 3000 WorldView-1 panchromatic images wall-to-wall in 1/2° mosaics for Tigray, Ethiopia. CA was evaluated with nearly 3000 WorldView-2 2 m multispectral 250 × 250 m image subsets by seven expert interpretations, and with in-situ global positioning system photography. CA estimates ranged from 32 to 41% in sub regions of Tigray with median maximum per bin commission and omission errors of 11% and 1% respectively, with most of the error occurring in bins <15%. This empirical, simple, and low direct cost approach via U.S. government license agreement to access commercial VHR data, could be a viable big-data high-performance computing methodology to extract wall-to-wall CA for other regions of the world that have very-small agriculture fields with similar image texture. [ABSTRACT FROM AUTHOR]

Abstract: The objective was to use digital color image texture features to predict troponin-T degradation in beef. Image texture features, including 88 gray level co-occurrence texture features, 81 two-dimension fast Fourier transformation texture features, and 48 Gabor wavelet filter texture features, were extracted from color images of beef strip steaks (longissimus dorsi, n=102) aged for 10d obtained using a digital camera and additional lighting. Steaks were designated degraded or not-degraded based on troponin-T degradation determined on d 3 and d 10 postmortem by immunoblotting. Statistical analysis (STEPWISE regression model) and artificial neural network (support vector machine model, SVM) methods were designed to classify protein degradation. The d 3 and d 10 STEPWISE models were 94% and 86% accurate, respectively, while the d 3 and d 10 SVM models were 63% and 71%, respectively, in predicting protein degradation in aged meat. STEPWISE and SVM models based on image texture features show potential to predict troponin-T degradation in meat. [Copyright &y& Elsevier]

The analysis of surface textures plays an important role in the geomorphometric analysis of high-resolution digital terrain models. Surface textures can be analyzed by means of geostatistical variogram-based indices. The use of variogram-based indices is promising because of their ability to consider the multiscale and anisotropic character of morphometric data. However, similar to other variance-type statistics, variogram-based indices are sensitive to the presence of hotspots and non-stationary data. Consequently, we present a multi-scale and directional image texture analysis operator (MAD or Median Absolute Differences) derived from a modification of a variogram estimator. MAD has been specifically developed to improve the robustness of variogram-based surface indices with a special focus on strongly non-stationary and often noisy spatial data representing solid earth surface morphology. Although the operator has been specifically developed for the analysis of high-resolution digital terrain models, it can be applied to the texture analysis of any type of image. Consequently MAD could be of interest in the broader context of remote sensing as well as for all disciplines for which image texture analysis is relevant. The theoretical presentation of the surface texture operator is accompanied by a working software prototype. The software prototype has been implemented in the Python scripting language for use in ArcGIS (ESRI) using its Spatial Analyst functions. The prototype architecture is concise and can be easily coded in different software environments, such as GIS mapping and image analysis software. The software prototype proposed has been developed to facilitate the development of ad hoc surface texture indices capable of adapting to the special needs of the study at hand. The MAD operator represents an improvement over variogram-based surface texture indices, offering a robust description of relevant aspects of surface texture, including surface roughness. [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]

The identification of interdicted nuclear or radioactive materials requires the application of dedicated techniques. In this work, a new approach for characterizing powder of uranium ore concentrates (UOCs) is presented. It is based on image texture analysis and multivariate data modelling. 26 different UOCs samples were evaluated applying the Angle Measure Technique (AMT) algorithm to extract textural features on samples images acquired at 250× and 1000× magnification by Scanning Electron Microscope (SEM). At both magnifications, this method proved effective to classify the different types of UOC powder based on the surface characteristics that depend on particle size, homogeneity, and graininess and are related to the composition and processes used in the production facilities. Using the outcome data from the application of the AMT algorithm, the total explained variance was higher than 90% with Principal Component Analysis (PCA), while partial least square discriminant analysis (PLS-DA) applied only on the 14 black colour UOCs powder samples, allowed their classification only on the basis of their surface texture features (sensitivity>0.6; specificity>0.6). This preliminary study shows that this method was able to distinguish samples with similar composition, but obtained from different facilities. The mean angle spectral data obtained by the image texture analysis using the AMT algorithm can be considered as a specific fingerprint or signature of UOCs and could be used for nuclear forensic investigation. [ABSTRACT FROM AUTHOR]

This article presents an image texture and hue statistical projection based retrieval. First the image is converted to HSI color model, the gray value of the image extraction, and Robert algorithm to extract the texture, then the image is divided into blocks and extracts the main color block, the main color image blocks are respectively projected in the horizontal and vertical direction of 2, get 2 projection histogram, the 2 projection histograms of the first three order center extraction distance and Robert algorithm as the features of texture, image similarity calculation. Make a very full pave the way for future Canny edge processing algorithm research of image retrieval. [ABSTRACT FROM AUTHOR]

Source camera identification enables forensic investigator to discover the probable source model that are employed to acquire the image under investigation. It is important whenever digital content is presented as a silent witness. In this paper, we present a source camera identification method via image texture features that are extracted from well selected color model and color channel. Except to distinguish source camera models from images whatever they are captured via same or different brand cameras, the main contributions of the proposed method are as follows: (1) It can distinguish imaging device individuals from images even if they are taken by using same brand and model of devices. (2) It is robust for content-preserving manipulations or geometric distortions, such as JEPG compression, adding noise, and rotation and scaling. The experimental results demonstrate that the performance of the proposed method is satisfactory. Compared with the state-of-the-art methods, the proposed method is superior in both detection accuracy and robustness. [ABSTRACT FROM AUTHOR]

Abstract: This study focused on finding the potential of image texture analysis for determining the spatiotemporal fracture behavior of crispy or crunchy food products. Two-dimensional stress distribution maps were obtained during compression failure of six dry-crisp samples at four loading stages. Applying gray-level co-occurrence matrix statistics to the stress distribution maps, four major textural features were extracted, which represent the spatial pattern of the stress intensity. Stress distribution maps for the samples were classified into original classes depending on their image textural features, using a canonical discriminant analysis with an accuracy of 99%. Local variations in stress intensity and the existence of areas with the same stress intensity at higher loading stages were critical spatial factors in discriminating among the different samples. In addition to spatial factors, temporal changes in image textural features during loading also provided information necessary for sample identification. This application of image texture analysis to stress distribution maps reveals the characteristics of spatiotemporal stress distribution accompanying the dynamic fracture process for crispy or crunchy samples. [Copyright &y& Elsevier]

Complexity measures, defined as measures of irregularity over time scales, are the subject of a growing number of studies as the information they reveal can find utility in a large field of applications. One of the most popular complexity measures is the multiscale entropy. Nevertheless, more and more algorithms dedicated to complexity analyses are proposed to improve the existing ones. However, such measures are available only for one-dimensional time series. For bidimensional data (images), no equivalent algorithm has been proposed to analyze irregularity over spatial scales. We herein introduce a new framework that extends the one-dimensional multiscale entropy (MSE 1D ) to the bidimensional case (MSE 2D ). Moreover, a variant of MSE 2D is also ModMSE 2D ). The two new algorithms are tested as new texture analysis frameworks. They are applied to simulated and real data. Our results show that, compared with other existing texture analysis algorithms, MSE 2D and ModMSE 2D are suitable and powerful tools for image analysis and classification according to their texture patterns. While MSE 2D is computationally faster than ModMSE 2D , ModMSE 2D is more robust to small image sizes. The two methods present interesting performances, and can be as useful as their unidimensional versions in two-dimensional applications. [ABSTRACT FROM AUTHOR]

We introduce a microstructure dataset focusing on complex, hierarchical structures found in a single Ultrahigh carbon steel under a range of heat treatments. Applying image representations from contemporary computer vision research to these microstructures, we discuss how both supervised and unsupervised machine learning techniques can be used to yield insight into microstructural trends and their relationship to processing conditions. We evaluate and compare keypoint-based and convolutional neural network representations by classifying microstructures according to their primary microconstituent, and by classifying a subset of the microstructures according to the annealing conditions that generated them. Using t-SNE, a nonlinear dimensionality reduction and visualization technique, we demonstrate graphical methods of exploring microstructure and processing datasets, and for understanding and interpreting high-dimensional microstructure representations. [ABSTRACT FROM AUTHOR]

The paper presents a new image classification technique which first extracts rotation-invariant image texture features in singular value decomposition (SVD) and discrete wavelet transform (DWT) domains. Subsequently, it exploits a support vector machine (SVM) to perform image texture classification. For convenience, it is called the SRITCSD method hereafter. First, the method applies the SVD to enhance image textures of an image. Then, it extracts the texture features in the DWT domain of the SVD version of the image. Also, the SRITCSD method employs the SVM to serve as a multiclassifier for image texture features. Meanwhile, the particle swarm optimization (PSO) algorithm is utilized to optimize the SRITCSD method, which is exploited to select a nearly optimal combination of features and a set of parameters utilized in the SVM. The experimental results demonstrate that the SRITCSD method can achieve satisfying results and outperform other existing methods under considerations here. [ABSTRACT FROM AUTHOR]

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]

Highlights: [•] Volume/image textural features were measured with computer-vision system. [•] Physical texture parameters and image texture features were measured with moisture. [•] Volume was not a good indicator of the end of drying process. [•] Eleven image texture features correlate well with moisture content (R 2 <0.9). [•] Uniformity of intensity texture feature can predict the end of drying process. [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]

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]

Highlights: [•] Image texture analysis is used to characterize micrographs and fractographs. [•] Fractal analysis, GLCM and RLS analysis have been carried out. [•] Systematic variation of texture descriptors with ageing temperature. [•] Correlation of texture descriptors vis-à-vis mechanical properties. [•] The concept of automatic characterization with machine vision has been highlighted. [ABSTRACT FROM AUTHOR]

Abstract: The methods currently used to characterise food breakdown during mastication are not easily applicable to cohesive and heterogeneous products such as white bread (baguettes). During this study, we investigated the applicability of image texture analysis to characterising the kinetics of bread bolus formation during chewing. Food boluses were collected from five subjects chewing four different breads after 10, 20 and 30 chewing cycles or at swallowing. Images were acquired and analysed using the Grey level co-occurrence matrix (GLCM) method. Food boluses were successfully discriminated: 60–73% of the images were correctly classified into their respective chewing cycles. Incorrect classifications arose from overlapping between subsequent cycles. Among the texture features the contrast was identified as being the best marker of food degradation. Different kinetics of bolus formation were observed between breads. This method revealed specific patterns of food degradation between subjects, which could be explained by their mastication behaviour and chewing efficiency. [Copyright &y& Elsevier]

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]

Abstract: Ecologists commonly collect data on vegetation structure, which is an important attribute for characterizing habitat. However, measuring vegetation structure across large areas is logistically difficult. Our goal was to evaluate the degree to which sample-point pixel values and image texture of remotely sensed data are associated with vegetation structure in a North American grassland–savanna–woodland mosaic. In the summers of 2008–2009 we collected vegetation structure measurements at 193 sample points from which we calculated foliage-height diversity and horizontal vegetation structure at Fort McCoy Military Installation, Wisconsin, USA. We also calculated sample-point pixel values and first- and second-order image texture measures, from two remotely sensed data sources: an infrared air photo (1-m resolution) and a Landsat TM satellite image (30-m resolution). We regressed foliage-height diversity against, and correlated horizontal vegetation structure with, sample-point pixel values and texture measures within and among habitats. Within grasslands, savanna, and woodland habitats, sample-point pixel values and image texture measures explained 26–60% of foliage-height diversity. Similarly, within habitats, sample-point pixel values and image texture measures were correlated with 40–70% of the variation of horizontal vegetation structure. Among habitats, the mean of the texture measure ‘second-order contrast’ from the air photo explained 79% of the variation in foliage-height diversity while ‘first-order variance’ from the air photo was correlated with 73% of horizontal vegetation structure. Our results suggest that sample-point pixel values and image texture measures calculated from remotely sensed data capture components of foliage-height diversity and horizontal vegetation structure within and among grassland, savanna, and woodland habitats. Vegetation structure, which is a key component of animal habitat, can thus be mapped using remotely sensed data. [Copyright &y& Elsevier]

Abstract: The images of commercial potato chips were evaluated for various colour and textural features to characterize and classify the appearance and to model the quality preferences of a group of consumers. Features derived from the image texture contained better information than colour features to discriminate both the quality categories of chips and consumers’ preferences. Entropy of a ∗ and V and energy of b ∗ from images of the total chip surface, average and variance of H and correlation of V from the images of spots and/or defects (if they are present), and average of L ∗ from clean images (chips free of spots and/or defects) showed the best correspondence with the four proposed appearance quality groups (A: ‘pale chips’, B: ‘slightly dark chips’, C: ‘chips with brown spots’, and D: ‘chips with natural defects’), giving classification rates of 95.8% for training data and 90% for validation data when linear discriminant analysis (LDA) was used as a selection criterion. The inclusion of independent colour and textural features from images of brown spots and/or defects and their clean regions of chips improved the resolution of the classification model and in particular to predict ‘chips with natural defects’. Consumers’ preferences showed that in spite of the ‘moderate’ agreement among raters (Kappa-value =0.51), textural features have potential to model consumer behaviour in the respect of visual preferences of potato chips. A stepwise logistic regression model was able to explain 86.2% of the preferences variability when classified into acceptable and non-acceptable chips. [Copyright &y& Elsevier]

Abstract: We tested image texture as a predictor of bird species richness in a semi-arid landscape of New Mexico. Bird species richness was summarized from 10-min point counts conducted at 12 points within 42 plots (108 ha each) from 1996 to 1998. We calculated 14 first- and second-order texture measures in eight different window sizes on a set of digital orthophotos acquired in 1996. For each of the 42 plots, we summarized mean and standard deviation of each texture value within multiple window sizes. The relationship between image texture and average bird species richness was assessed using linear regression models. Single image texture measures such as the standard deviation described up to 57% of the variability in species richness. Coupling multiple measures of texture or coupling elevation with a single texture measure described up to 63% of the variability in bird species richness. Models incorporating two measures of texture and coarse habitat type described 76% of the variability in bird species richness. These results show that image texture analysis is a very promising tool for characterizing habitat structure and predicting patterns of species richness in semi-arid ecosystems. This method has several advantages over methods that rely on classified imagery, including cost-effectiveness, incorporation of within-habitat vegetation variability, and elimination of errors associated with boundary delineation. [Copyright &y& Elsevier]

The Kolmogorov–Sinai (K–S) entropy is used to quantify the average amount of uncertainty of a dynamical system through a sequence of observations. Sequence probabilities therefore play a central role for the computation of the entropy rate to determine if the dynamical system under study is deterministically non-chaotic, deterministically chaotic, or random. This paper extends the notion of the K–S entropy to measure the entropy rate of imprecise systems using sequence membership grades, in which the underlying deterministic paradigm is replaced with the degree of fuzziness. While constructing sequential probabilities for the calculation of the K–S entropy is difficult in practice, the estimate of the K–S entropy in the setting of fuzzy sets in an image is feasible and can be useful for modeling uncertainty of pixel distributions in images. The fuzzy K–S entropy is illustrated as an effective feature for image analysis and texture classification. [ABSTRACT FROM AUTHOR]

Abstract: Avian biodiversity is threatened, and in order to prioritize limited conservation resources and conduct effective conservation planning a better understanding of avian species richness patterns is needed. The use of image texture measures, as a proxy for the spatial structure of land cover and vegetation, has proven useful in explaining patterns of avian abundance and species richness. However, prior studies that modeled habitat with texture measures were conducted over small geographical extents and typically focused on a single habitat type. Our goal was to evaluate the performance of texture measures over broad spatial extents and across multiple habitat types with varying levels of vertical habitat structure. We calculated a suite of texture measures from 114 Landsat images over a study area of 1,498,000km2 in the Midwestern United States, which included habitats ranging from grassland to forest. Avian species richness was modeled for several functional guilds as a function of image texture. We subsequently compared the explanatory power of texture-only models with models fitted using landscape composition metrics derived from the National Land Cover Dataset, as well as models fitted using both texture and composition metrics. Measures of image texture were effective in modeling spatial patterns of avian species richness in multiple habitat types, explaining up to 51% of the variability in species richness of permanent resident birds. In comparison, landscape composition metrics explained up to 56% of the variability in permanent resident species richness. In the most heavily forested ecoregion, texture-measures outperformed landscape metrics, and the two types of measurements were complementary in multivariate models. However, in two out of three ecoregions examined, landscape composition metrics consistently performed slightly better than texture measures, and the variance explained by the two types of measures overlapped considerably. These results show that image texture measures derived from satellite imagery can be an important tool for modeling patterns of avian species richness at broad spatial extents, and thus assist in conservation planning. However, texture measures were slightly inferior to landscape composition metrics in about three-fourths of our models. Therefore texture measures are best considered in conjunction with landscape metrics (if available) and are best used when they show explanatory ability that is complementarity to landscape metrics. [Copyright &y& Elsevier]

Abstract: Estimation of forest structural parameters by field-based data collection methods is both expensive and time consuming. Satellite remote sensing is a low-cost alternative in modeling and mapping structural parameters in large forest areas. The current study investigates the potential of using WordView-2 multispectral satellite imagery for predicting forest structural parameters in a dryland plantation forest in Israel. The relationships between image texture features and the several structural parameters such as Number of Trees (NT), Basal Area (BA), Stem Volume (SV), Clark-Evans Index (CEI), Diameter Differentiation Index (DDI), Contagion Index (CI), Gini Coefficient (GC), and Standard Deviation of Diameters at Breast Heights (SDDBH) were examined using correlation analyses. These variables were obtained from 30m×30m square-shaped plots. The Standard Deviation of Gray Levels (SDGL) as a first order texture feature and the second order texture variables based on Gray Level Co-occurrence Matrix (GLCM) were calculated for the pixels that corresponds to field plots. The results of the correlation analysis indicate that the forest structural parameters are significantly correlated with the image texture features. The highest correlation coefficients were calculated for the relationships between the SDDBH and the contrast of red band (r =0.75, p <0.01), the BA and the entropy of blue band (r =0.73, p <0.01), and the GC and the contrast of blue band (r =0.71, p <0.01). Each forest structural parameter was modeled as a function of texture measures derived from the satellite image using stepwise multi linear regression analyses. The determination coefficient (R 2) and root mean square error (RMSE) values of the best fitting models, respectively, are 0.38 and 109.56ha−1 for the NT; 0.54 and 1.79m2 ha−1 for the BA; 0.42 and 27.18m3 ha−1 for the SV; 0.23 and 0.16 for the CEI; 0.32 and 0.05 for the DDI; 0.25 and 0.06 for the CI; 0.50 and 0.05 for the GC; and 0.67 and 0.70 for the SDDBH. The leave-one-out cross-validation technique was applied for validation of the best-fitted models (R 2 >0.50). In conclusion, cross-validated statistics confirmed that the structural parameters including the BA, SDDBH, and GC can be predicted and mapped with a reasonable accuracy using the texture features extracted from the spectral bands of WorldView-2 image. [Copyright &y& Elsevier]

The K-means Iterative Fisher (KIF) algorithm is a robust, unsupervised clustering algorithm applied here to the problem of image texture segmentation. The KIF algorithm involves two steps. First, K-means is applied. Second, the K-means class assignments are used to estimate parameters required for a Fisher linear discriminant (FLD). The FLD is applied iteratively to improve the solution. This combined K-means and iterative FLD is referred to as the KIF algorithm. Two KIF implementations are presented: a mixture resolving approach is extended to an unsupervised binary hierarchical approach. The same binary hierarchical KIF algorithm is used to properly segment images even though the number of classes, the class spatial boundaries, and the number of samples per class vary. The binary hierarchical KIF algorithm is fully unsupervised, requires no a priori knowledge of the number of classes, is a non-parametric solution, and is computationally efficient compared to other methods used for clustering in image texture segmentation solutions. This unsupervised methodology is demonstrated to be an improvement over other published texture segmentation results using a wide variety of test imagery. Gabor filters and co-occurrence probabilities are used as texture features. [Copyright &y& Elsevier]

Abstract: The objective of this study was to investigate the usefulness of raw meat surface characteristics (texture) in predicting cooked beef tenderness. Color and multispectral texture features, including 4 different wavelengths and 217 image texture features, were extracted from 2 laboratory-based multispectral camera imaging systems. Steaks were segregated into tough and tender classification groups based on Warner–Bratzler shear force. The texture features were submitted to STEPWISE multiple regression and support vector machine (SVM) analyses to establish prediction models for beef tenderness. A subsample (80%) of tender or tough classified steaks were used to train models which were then validated on the remaining (20%) test steaks. For color images, the SVM model correctly identified tender steaks with 100% accurately while the STEPWISE equation identified 94.9% of the tender steaks correctly. For multispectral images, the SVM model predicted 91% and STEPWISE predicted 87% average accuracy of beef tender. [Copyright &y& Elsevier]

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]

Abstract: Color images can be treated as two-dimensional quaternion functions. For analysis of quaternion images, a joint space-wavenumber localized quaternion S transform (QS) is presented in this study for a simultaneous determination of the local color image spectra. The QS transform uses a two-dimensional Gaussian localizing window that scales with wavenumbers. Rotation invariance, invertibility and computational aspects of the QS transform are discussed. The power map of the QS transform is presented here as a powerful tool in color image texture and pattern analysis. Examples are presented. [Copyright &y& Elsevier]

Abstract: The intima–media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). It was proposed but not thoroughly investigated that the composition and texture of the media layer (ML) can be used as an indicator for the risk of stroke. In this study, we investigate the application of texture analysis of the ML of the CCA and how texture is affected by age and gender. The study was performed on 100 longitudinal-section ultrasound images acquired from asymptomatic subjects at risk of atherosclerosis. The images were separated into three different age groups, namely below 50, 50–60, and above 60 years old. Furthermore, the images were separated according to gender. A total of 61 different texture features were extracted from the intima layer (IL), the ML, and the intima–media complex (IMC). The ML and the IMC were segmented manually by a neurovascular expert and also automatically by a snakes segmentation system. We have found that male patients tended to have larger media layer thickness (MLT) values as compared to the MLT of female patients of the same age. We have found significant differences among texture features extracted from the IL, ML and IMC from different age groups. Furthermore, for some texture features, we found that they follow trends that correlate with a patient''s age. For example, the gray-scale median GSM of the ML falls linearly with increasing MLT and with increasing age. Our findings suggest that ultrasound image texture analysis of the media layer has potential as an assessment biomarker for the risk of stroke. [Copyright &y& Elsevier]

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: Multi-spectral imaging technique was applied to sorting the green tea category. 320 images were captured at three wavelengths (580, 680 and 800nm) using a multi-spectral digital camera. Entropy values of images were obtained as image texture features. The correction answer rate of least squares-support vector machine (LS-SVM) with radial basis function kernel was up to 100% which was better than those of LS-SVM with linear kernel, partial least squares and radial basis function neural networks, respectively. Results of generation ability test shows that LS-SVM with radial basis function kernel could be effectively used for the application on a few samples. It could be concluded that it is possible to take multi-spectral images of tea and tell which category it is. The whole process is simple, fast, non-destructive and easy to operate. [Copyright &y& Elsevier]

• 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]

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]

Abstract: Texture is an important image feature and has been applied greatly in the food industry for quality evaluation and inspection. Recent applications of texture-analysis techniques in the food industry were reviewed in the current paper. There are four different types of texture, i.e. statistical texture, structural texture, model-based texture, and transform-based texture. Statistical texture can be obtained using statistical approachs from the higher-order of pixel grey values of images while structural texture is acquired through some structural primitives constructed from grey values of pixels. Transform-based texture can be obtained by using statistical measurements from the images transformed with certain methods. Model-based texture can be attained by calculating coefficients from a model based on the relationship of the grey values between a pixel and its neighboring pixels. Among them, statistical texture is the most widely used one in the food industry for its high accuracy and less computation time. Transform-based texture and model-based texture are also used, although not as popular as statistical texture. However, structural texture is rarely used in the food industry. Due to the limited understanding of image texture up to now, it is difficult to identify which kind of method is the best for certain application. Therefore, it is expected that by employing different methods together, final results can be improved. [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]

Addressing global declines in biodiversity requires accurate assessments of key environmental attributes determining patterns of species diversity. Spatial heterogeneity of vegetation strongly affects species diversity patterns, and measures of vegetation structure derived from lidar and satellite image texture analysis correlate well with species richness. Our goal here was to gain a better understanding of why image texture explains bird richness, by linking field-based measures of vegetation structure directly with both image texture and bird richness. In addition, we asked how image texture compares with lidar-based canopy height variability, and how sensor resolution affects the explanatory power of image texture. We generated texture metrics from 30 m (Landsat 8) and 10 m (Sentinel-2) resolution Enhanced Vegetation Index (EVI) imagery from 2017 to 2019. We compared textures with vegetation metrics and bird richness data from 27 National Ecological Observatory Network (NEON) terrestrial field sites across the continental US. Both 30 and 10 m resolution texture metrics were strongly correlated with lidar-based canopy height variability (|r| = 0.64 and 0.80, respectively). Texture was moderately correlated with field-based metrics, including variability of vegetation height and tree stem diameter, and foliage height diversity (range |r| = 0.31–0.52). Generally, 10 m resolution texture had stronger correlations with lidar and field-based metrics than 30 m resolution texture. In univariate linear models of total bird richness, 10 m resolution texture metrics also had higher explanatory power (up to R 2 adj = 0.45), than 30 m texture metrics (up to R 2 adj = 0.31). Among all metrics evaluated, the 10 m homogeneity texture was the best univariate predictor of total bird richness. In multivariate bird richness models that combined texture with lidar-based canopy height variability and field-based metrics, both 30 m and 10 m resolution texture metrics were selected in top-ranked models and independently contributed explanatory power (up to R 2 adj = 46%). Lidar-based canopy height variability was also selected in a top-ranked model of total bird richness, but independently contributed only 15% of the variance explained. Our results show satellite image texture characterized multiple features of structural and compositional vegetation heterogeneity, complemented more commonly used metrics in models of bird richness and for some guilds outperformed both lidar-based canopy height variability and field-based vegetation measurements. Ours is the first study to directly link image texture both to specific components of vegetation heterogeneity and to bird richness across multiple ecoregions and spatial resolutions, thereby shedding light on habitat features underlying the strong correlation between image texture and biodiversity. • Image texture captures heterogeneity in both vegetation structure and composition. • 10 m resolution texture outperforms 30 m texture in bird richness models. • Texture metrics outperform lidar canopy height variability in bird richness models. • Image texture has exciting potential for biodiversity research and conservation. [ABSTRACT FROM AUTHOR]

Raveling is one of the critical and pervasive modes of failure observed in asphalt pavement road. Automatic detection of raveling based on image samples is a challenging task due to the complex texture of asphalt pavement. This study constructs and investigates the capability of an image processing based approach for raveling recognition. Image texture based features extracted from statistical properties of color channels and the Gray-Level Co-Occurrence Matrix are employed as input variables to characterize the state of pavement. The Stochastic Gradient Descent Logistic Regression (SGD-LR) is used to classify image samples into two categories of non-raveling and raveling based on a set of extracted features. A SGD-LR based raveling detection program has been developed in Visual C#.NET to facilitate its implementation. Experimental outcome shows that the newly constructed approach can attain a good predictive performance with a classification accuracy rate of roughly 88%. Therefore, this approach can be a helpful tool to assist transportation authorities in the task of surveying asphalt pavement condition. The Stochastic Gradient Descent (SGD) algorithm Graphical user interface of the SGD-LR program used for pavement raveling detection. Unlabelled Image • Image processing based raveling detection is proposed. • Image texture analysis is used in feature extraction phase. • Stochastic Gradient Descent Logistic Regression is used for classification. • The software has been developed in Visual C#.NET. • The program has achieved good detection accuracy of 88.12%. [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]

Highlights: [•] Froth texture extraction based on color co-occurrence matrix (CCM) is proposed. [•] The texture complexity based on CCM is used to describe the surface froth texture. [•] The relation between the texture complexity and the concentrate grade is studied. [•] The optimal texture complexity range is obtained to instruct the flotation operation. [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]

The use of computer vision technology has been highly successful in food classification problems in the past and it has continued this success in recent times. There does however exist a number of opportunities to progress computer vision technology further, these opportunities are critically examined based on cost and feasibility. A range of hardware options are considered along with a range of software options. The economic cost of implementing new hardware continues to prove a major impediment. Thus future efforts need to be focused on maximising the potential benefits of the existing hardware framework and instead concentrate on developing improved software. Of the improved software available the aspect that offers the greatest promise is more efficient analysis of food surface texture attributes which will lead to more powerful understanding of the relationships between quality factors and experimentally measured food quality. [Copyright &y& Elsevier]

Abstract: A method is proposed for quantitative description of blood-vessel trees, which can be used for tree classification and/or physical parameters indirect monitoring. The method is based on texture analysis of 3D images of the trees. Several types of trees were defined, with distinct tree parameters (number of terminal branches, blood viscosity, input and output flow). A number of trees were computer-simulated for each type. 3D image was computed for each tree and its texture features were calculated. Best discriminating features were found and applied to 1-NN nearest neighbor classifier. It was demonstrated that (i) tree images can be correctly classified for realistic signal-to-noise ratio, (ii) some texture features are monotonously related to tree parameters, (iii) 2D texture analysis is not sufficient to represent the trees in the discussed sense. Moreover, applicability of texture model to quantitative description of vascularity images was also supported by unsupervised exploratory analysis. Eventually, the experimental confirmation was done, with the use of confocal microscopy images of rat brain vasculature. Several classes of brain tissue were clearly distinguished based on 3D texture numerical parameters, including control and different kinds of tumours – treated with NG2 proteoglycan to promote angiogenesis-dependent growth of the abnormal tissue. The method, applied to magnetic resonance imaging e.g. real neovasculature or retinal images can be used to support noninvasive medical diagnosis of vascular system diseases. [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]

Abstract: Quantifying the above ground biomass of tropical forests is critical for understanding the dynamics of carbon fluxes between terrestrial ecosystems and the atmosphere, as well as monitoring ecosystem responses to environmental change. Remote sensing remains an attractive tool for estimating tropical forest biomass but relationships and methods used at one site have not always proved applicable to other locations. This lack of a widely applicable general relationship limits the operational use of remote sensing as a method for biomass estimation, particularly in high biomass ecosystems. Here, multispectral Landsat TM and JERS-1 SAR data were used together to estimate tropical forest biomass at three separate geographical locations: Brazil, Malaysia and Thailand. Texture measures were derived from the JERS-1 SAR data using both wavelet analysis and Grey Level Co-occurrence Matrix methods, and coupled with multispectral data to provide inputs to artificial neural networks that were trained under four different training scenarios and validated using biomass measured from 144 field plots. When trained and tested with data collected from the same location, the addition of SAR texture to multispectral data showed strong correlations with above ground biomass (r =0.79, 0.79 and 0.84 for Thailand, Malaysia and Brazil respectively). Also, when networks were trained and tested with data from all three sites, the strength of correlation (r =0.55) was stronger than previously reported results from the same sites that used multispectral data only. Uncertainty in estimating AGB from different allometric equations was also tested but found to have little effect on the strength of the relationships observed. The results suggest that the inclusion of SAR texture with multispectral data can go someway towards providing relationships that are transferable across time and space, but that further work is required if satellite remote sensing is to provide robust and reliable methodologies for initiatives such as Reducing Emissions from Deforestation and Degradation (REDD+). [Copyright &y& Elsevier]