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The presence of unburned carbon in coal-burning systems undoubtedly causes a loss in the amount of energy that can be obtained from the system, and also reveals an inadequacy in terms of the usability of the ashes. The expensiveness of existing unburned carbon prediction methods is one of the reasons why these technologies cannot be used. This situation requires working on alternative non-combustible carbon technologies. In this paper, a new approach is presented for estimating unburned carbon in a small-scale coal burner system using the Gaussian regression model and CCD camera-acquired flame image. The proposed approach evaluates brightness, fluctuation amplitude, area, and radiation signal properties of the flame image. The proposed non-combustible carbon estimation technique does not require prior knowledge of CCD camera features. In the feature acquisition phase, results were obtained for each natural component of the flame image in RGB colour space separately, in pairs, all together and for three artificial colour channels (grey image). With the proposed method, the unburned carbon estimation was obtained with an accuracy of R = 0.9664 when all colour channels of the RGB image were used together. This result shows that unburned carbon can be estimated from the instantaneous flame images obtained with the CCD camera. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a statistical model that predicts the CPX-Level at 80 km/h of porous and semi-porous mixes as a function of porosity and texture band levels. The model was calibrated using data from six pavement sections laid on Italian motorway corridors, two paved with open-graded mixes and four with semi-open-graded mixes. The parameter representative of porosity is the ratio between the voids in the compacted mix (Va) and the Voids in Mineral Aggregate (VMA). The texture band levels were defined upon a statistical analysis comprising the correlation coefficient, the Mallows coefficient and the analysis of variance. The results show that the main texture band levels involved belong to the range of macrotexture and that the model provides adequate accuracy, which improves when the equation is used with average values along the test sections of 200 metres. [ABSTRACT FROM AUTHOR]

Methodology for analysing textural properties of sugar beet roots in the laboratory has previously been established. It has been shown to be reliable and of value in exploring relationships between textural properties, damage rates, and storability of varieties. In this paper, a methodology for the assessment of textural properties in-field, prior to harvest, using an inexpensive handheld penetrometer is examined. Three sugar beet varieties were grown in Belgium, the Netherlands, and Sweden during 2019. Textural properties were assessed in-field with the handheld penetrometer 2, 1 and 0 months prior to harvest, and with the laboratory penetrometer directly after harvest. Comparison of the results showed generally strong correlations. A power analysis suggests a difference in mean Handheld Pressure of 0.10 MPa could be found significant within a large trial with a block design. The reliability of the handheld penetrometer was further assessed in the Swedish national variety trials over three years (2019-2021). Correlation coefficients of 0.86 and 0.94 were found between mean Handheld Pressure for 2019 and 2020, and 2020 and 2021 respectively. The handheld penetrometer can be applied as an economic means of quantifying differences in textural properties of sugar beet varieties. Clear operating procedure and training must exist. [ABSTRACT FROM AUTHOR]

This paper presents two novel contrast enhancement approaches using texture regions-based histogram equalization (HE). In HE-based contrast enhancement methods, the enhanced image often contains undesirable artefacts because an excessive number of pixels in the non-textured areas heavily bias the histogram. The novel idea presented in this paper is to suppress the impact of pixels in non-textured areas and to exploit texture features for the computation of histogram in the process of HE. The first algorithm named as Dominant Orientation-based Texture Histogram Equalization (DOTHE), constructs the histogram of the image using only those image patches having dominant orientation. DOTHE categories image patches into smooth, dominant or non-dominant orientation patches by using the image variance and singular value decomposition algorithm and utilizes only dominant orientation patches in the process of HE. The second method termed as Edge-based Texture Histogram Equalization, calculates significant edges in the image and constructs the histogram using the grey levels present in the neighbourhood of edges. The cumulative density function of the histogram formed from texture features is mapped on the entire dynamic range of the input image to produce the contrast-enhanced image. Subjective as well as objective performance assessment of proposed methods is conducted and compared with other existing HE methods. The performance assessment in terms of visual quality, contrast improvement index, entropy and measure of enhancement reveals that the proposed methods outperform the existing HE methods. [ABSTRACT FROM AUTHOR]

Urban trees have the potential to mitigate some of the harm brought about by rapid urbanization and population growth, as well as serious environmental degradation (e.g. soil erosion, carbon pollution and species extirpation), in cities. This paper presents a novel urban tree extraction modelling approach that uses discrete laser scanning point clouds and object-based textural analysis to (1) develop a model characterised by four sub-models, including (a) height-based split segmentation, (b) feature extraction, (c) texture analysis and (d) classification, and (2) apply this model to classify urban trees. The canopy height model is integrated with the object-level local binary pattern algorithm (LBP) to achieve high classification accuracy. The results of each sub-model reveal that the classification of urban trees based on the height at 47.14 (high) and 2.12 m (low), respectively, while based on crown widths were highest and lowest at 22.5 and 2.55 m, respectively. Results also indicate that the proposed algorithm of urban tree modelling is effective for practical use. [ABSTRACT FROM AUTHOR]

This paper proposes a novel distortion function for JPEG steganography, which depends on the magnitude of discrete cosine transformation (DCT) coefficients, the first- and second-order residuals, and the amount of zeros in a DCT block. The magnitude of a DCT coefficient indicates the ability of the current coefficient to conceal modification trace. The first- and second-order residuals make sufficient use of the correlation in DCT domain, and the amount of zeros in a DCT block illustrates the block texture well. These elements are combined together to measure the risks of detection due to the modification on the cover data. By using this proposed distortion function, the embedding changes are limited in texture regions when employing the syndrome trellis coding to embed secret data. Thus less detectable artefacts could be found in stego images. The experimental results demonstrate that the proposed scheme performs less statistical detectability when comparing to current state-of-the-art steganographic methods for JPEG images. [ABSTRACT FROM AUTHOR]

Aiming at the problem of digital inheritance and innovation of batik fabric patterns, a method for extracting batik fabric pattern and element was proposed. First, performing morphological preprocessing on the obtained digital image of batik fabric, including morphological gradient, reconstruction and local maximum image acquisition and adjustment, not only effectively remove the texture and noise in the image, but also get a binary image that can clearly reflect the overall characteristics of the pattern and elements. Then, for the complete elements in the image, according to the labeled number, the element is reserved, and other elements are set as the background to extract each element in the image, and generate an independent elements image. Finally, the canny algorithm was used to extract the overall contour of the batik fabric pattern and elements to obtain pattern contour and elements contour that can be read and edited independently. Experimental results for different batik fabric images and image segmentation methods proved the effectiveness of the method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a context-driven injection scheme for pansharpening, in which the injection coefficients are computed over superpixel segments obtained by means of a modified Simple Linear Iterative Clustering (t-SLIC) technique applied on the texture descriptors of the PAN image. By using the t-SLIC algorithm, various homogeneous-connected components can be generated according to their spectral properties. The proposed pansharpening method relies on a multiresolution framework by employing the Generalized Laplacian Pyramid (GLP) tailored to the Modulation Transfer Function (MTF) of the MS sensors for extracting the high frequency details. First, the injection gains are locally computed as regression coefficients between the upsampled MS and low-resolution PAN regions at a reduced scale. Then, they are multiplied by a global weighting factor computed per spectral band and defined as the ratio of variance between expanded MS bands and PAN image. Finally, the spatial details are modulated by means of the estimated global-local injection coefficients at superpixel level to produce the high-resolution MS image. The validation is assessed with two datasets acquired by IKONOS and WorldView-3 satellites. The experimental results show that the proposed method achieves a favourable performance both visually and quantitatively compared to the state of-the-art pansharpening algorithms. [ABSTRACT FROM AUTHOR]

Multiresolution analysis (MRA) methods have been successfully used in texture analysis. Texture analysis is widely discussed in literature, but most of the methods which do not employ multiresolution strategy cannot exploit the fact that texture occurs at various spatial scales. This paper proposes a methodology to identify different classes in satellite images using texture features from newly developed multiresolution methods. The proposed method is tested on remotely sensed optical images and a Pauli RGB decomposed version of synthetic aperture radar image. The textural information is extracted at various scales and in different directions from curvelet and contourlet transforms. The results are compared with wavelet-based features. Accuracy assessment is performed and comparative analysis is carried out using minimum distance to mean, support vector machine and random forest classifiers. It is found that the proposed method shows better class discriminating power and classification capability as compared to existing wavelet-based method. [ABSTRACT FROM AUTHOR]

The rapid and accurate detection of damaged buildings after an earthquake is critical for emergency response. As roof textures of damaged buildings are changed, damaged buildings may be accurately detected by the texture heterogeneity among buildings. However, existing researches have scarcely detected damaged buildings quantitatively by the texture heterogeneity among buildings from post-earthquake images. Therefore, this paper proposes an approach for the detection of earthquake-damaged buildings using texture feature contribution from post-earthquake images. In the proposed approach, a texture feature contribution index (TFCI) is defined by the information gain for selection of the optimal texture features, and a building texture damage index (TFdam) is defined for detection of damaged buildings. The proposed approach primarily comprises the following three steps: 1) building image objects are obtained using a pre-earthquake vector map to segment post-earthquake images; 2) the optimal texture features are selected by the TFCI to establish the texture feature vector; 3) damaged buildings are detected using the TFdam. The town of Leigu in Sichuan Province was selected as the study area to validate the performance of the proposed approach. The accuracy of 93.45% and kappa coefficient of 0.869 were achieved. Furthermore, the results indicate that the proposed approach can accurately detect earthquake-damaged buildings. [ABSTRACT FROM AUTHOR]

Texture is one of the fundamental features for describing image characteristics. Face can be seen as a composition of micro-patterns of textures. The texture analysis community has proposed a variety of descriptors for face recognition. Local binary pattern (LBP) is a very popular texture operator used in a wide variety of applications including face recognition. Many variants of LBP have been proposed so far and let more emerges due to its overwhelming success. In this series, a new face recognition algorithm called local derivative ternary pattern (LDTP) is proposed in this paper in order to alleviate the face recognition rate under real-time challenges. The strength of the descriptor is demonstrated on four different databases containing more than 2000 face images under variations in lighting, facial expression, and pose. The experimental results show that the proposed LDTP approach provides a better representation of face patterns and achieves higher recognition rates than LBP and its derivatives. [ABSTRACT FROM PUBLISHER]

In this paper, a new pansharpening method is proposed to obtain high resolution multi spectral image, while preserving spectral signature. Spectral information is considered to be a piecewise smooth area with almost clear boundaries, and is represented in the cartoon space. Spatial information is described by the orthogonal complement of the cartoon space, which is the texture space. Therefore, remote sensing (RS) images can be transferred to a new space by the cartoon plus texture (CPT) decomposition, where spatial and spectral information can be discriminated properly, and pansharpening is done by substituting multispectral texture components with panchromatic texture component. However, cartoon components of the multispectral bands remains unchanged, and the edges and boundaries of the fused image do not sharpen enough. Therefore, we propose a new decomposition model, using the gradient of the panchromatic cartoon component, to fortify boundaries and some important details, while preserving spectral quality. The proposed method is compared with the well-known classic pansharpening methods. Experimental results show that the proposed method has a better performance in terms of both spatial and spectral qualities, however, it is not efficient in terms of computing time. [ABSTRACT FROM PUBLISHER]

Land cover classification is yet a challenging task due to complex landscapes, less a priori knowledge and complicated data sets. Researchers are utilising scattering-based model, polarimetric-based model and empirical model for classification purpose on SAR (Synthetic Aperture Radar) data, but achieving good classification accuracy requires more consideration. Most of the work reported has been performed by either utilising only single polarisation channel or on the composite image, due to which the significance of each channel remains unidentified. Previously, SAR feature-based land cover characterisation has shown favourable results, by examining the use of one or two forms of features and not much attempt has been dedicated to the simultaneous incorporation of multiple types of texture and wavelet features in a manner that preserves all the significant segregation information for land cover classification. Therefore, in this paper, different statistical, textural and wavelet features were analysed on three individual polarimetric channels (HH, HV and VV). For enhancing the discrimination capability, principal component analysis and linear discriminant analysis have been employed and the classification information attained from the three polarimetric channels was fused for accounting the implication of each of them. Proposed methodology is screening decent classification accuracy of 80.83% with kappa coefficient 0.7289, with less complexity and easy implementation. [ABSTRACT FROM AUTHOR]

Theoretical calculations confirmed that the adsorption of the benzimidazolium salt inhibitor molecules on iron surface was produced in multiple active sites, and the bonds between the N, C atoms in the inhibitor molecule and the metal Fe atom is relatively stable. The objective of the special issue is to report on the advances in experimental, theoretical, and computational aspects including (i) Organic and plant extract corrosion inhibitors; (ii) Metal coating formulations and techniques; (iii) I In situ i real-time monitoring of corrosion. The corrosion inhibition efficiency of I Ganoderma lucidum i crude extract was better by more than 10% than that of triterpenoid extract, and the carboxyl oxygen, hydroxyl, and electrons in triterpenoids have high binding energies to Fe atoms. [Extracted from the article]

Digital image acquisition is now a simple task and information in the form of digital images is drastically increasing on social media, which has both positive and negative impacts on a society in many different ways. Advanced user-friendly tools have made it easy to manipulate image content in order to gain illegal advantage or to make false propaganda, and digital images and videos are not acceptable in courts of law as evidence without reliable forensic analysis. A lot of research has been done in order to address this problem and many techniques exist that detect and localize copy-move and splicing forgeries. However, it is very important to know whether these methods are robust, properly modelling the structural changes that have occurred in images due to copy-move and/or splicing forgeries, and can reliably classify a digital image as a genuine or modified image. In this paper, we present an extensive literature review of the state-of-the-art techniques on copy-move and splicing forgeries, highlighting their limitations, and we provide future research directions. [ABSTRACT FROM AUTHOR]

The goal of this paper is to introduce and demonstrate a new high-performance super-resolution (SR) method for multi-frame images. By combining learning-based and reconstruction-based SR methods, this paper proposes a multi-frame image super-resolution method based on adaptive self-learning. Using the adaptive self-learning method and recovery of high-frequency edge information, an initial high-resolution (HR) image containing effective texture information is obtained. The edge smoothness prior is then used to satisfy the global reconstruction constraint and enhance the quality of the HR image. Our results indicate that this method achieves better performance than several other methods for both simulated data and real-scene images. [ABSTRACT FROM AUTHOR]

Building extraction from remote sensing images is very important in many fields, such as urban planning, land use investigation, damage assessment, and so on. In polarimetric synthetic aperture radar (PolSAR) imagery, the buildings not only have typical polarimetric features but also have rich texture features. In this paper, the texture information is introduced to improve the accuracy of urban building extraction from PolSAR imagery by a new method calledcross reclassification. Based on this method, the polarimetric information-based results and texture-based results can be effectively fused. The experimental results of three representative PolSAR images with different characteristics demonstrate the effectiveness of the proposed method, and the accuracy of building extraction can be improved, compared with the traditional method using only polarimetric information. [ABSTRACT FROM AUTHOR]

Manually building models of complex objects is usually very difficult. 3D scanning can provide accurate models, but the required processing of very large data sets is time-consuming and not suitable for real-time applications. This paper presents a novel method that uses a series of images to represent an object. Initially, we obtain many images by photographing in different spatial directions. Each image matches a vector of an observed direction. During real-time rendering, the image closest to the eye direction is selected and shown. We call this method of model building the image-view pairs (IVP) model. The IVP model can support dynamic shadows in addition to static shadows. This paper proposes a method based upon affine transformation and multitexturing. IVP objects also support collision detection. Experiments show that the proposed IVP objects look realistic and require less processing power and memory. Moreover, the displayed scene runs very smoothly. The proposed method can be very useful for large real-time applications. [ABSTRACT FROM AUTHOR]

Unsupervised texture segmentation is a challenging topic in computer vision. It is difficult to obtain boundaries of texture regions automatically in real-time, especially for cluttered images. This paper presents a new fast unsupervised texture segmentation method. First, the Texel similarity map (TSM) is used to compare the changes of intensity and gray level of neighboring pixels to determine whether they are identical. Then, a scheme called multiple directions integral images (MDII) is proposed to quickly evaluate the TSM. With the aid of MDII, one pixel’s similarity value can be computed in constant time. Finally, the proposed segmentation method is tested on both artificial texture and natural images. Experimental results show that the proposed method performs well on a wide range of images, and outperforms state-of-the-art method on segmentation speed. [ABSTRACT FROM AUTHOR]

We present a technique to recover and refine the depth map from a single image captured by a conventional camera in this paper. Our method builds on the universal imaging principle: only scene at the focus distance will converge to a single sharp point on imaging sensor but other scene will yield different blur effects varying with its distance from the camera lens. We first estimate depth values at edge locations via spectrum contrast and then recover the full depth map using a depth matting optimization method. Due to the fact that some blur textures such as soft shadows or blur patterns will produce ambiguity results during the procedure of depth estimation, we use a total variation-based image smoothing method to smooth the original image, a smoothed image with detailed texture being suppressed can be generated. Taking this smoothed image as reference image, a guided filter is used to refine the final depth map. [ABSTRACT FROM PUBLISHER]

Video surveillance is one of the major applications where high-resolution (HR) images are crucial. Since the video camera has limited spatial and temporal resolution, there is a need for super resolution video generation algorithms. In this paper, we have presented a novel technique for activity detection in the surveillance video. To achieve this goal, we have proposed and investigated efficient algorithms for Video Object Plane (VOP) generation, shadow removal from VOP and super-resolved VOP generation, for activity detection from surveillance video. The proposed VOP generation algorithm is computationally efficient and works for both dynamic and static backgrounds. The novel shadow removal algorithm for the VOP is based on texture and its performance has been studied based on average shadow detection and discrimination rates. The proposed super-resolution video generation algorithm has been designed using edge models. The performance of this algorithm has been evaluated using a numerical analysis technique and is found to be better than bi-cubic and bi-linear interpolation techniques. [ABSTRACT FROM AUTHOR]

In recent years, object detection and tracking has been a dynamic research area. Rapid development of the multimedia and the associated technologies urge the processing of a huge database of video clips. The processing efficiency lies on the search methodologies utilised in the video processing system. Usage of unsuitable search methodologies may make the processing system ineffective. Hence, effective object detection and tracking system is an essential criterion for searching relevant videos from a huge collection of videos. This paper proposes a unique object detection and tracking system where video segmentation, feature extraction, object detection and tracking are combined perfectly using various features. Initially, the database video clips are segmented into different shots before performing the feature extraction process. The proposed system consists of two stages, namely, feature extraction and tracking of object in the video clips. In the feature extraction stage, firstly, colour feature is extracted based on colour quantisation. Next, edge density feature is extracted for the objects present in the query video. Then, the texture feature is extracted based on LGXP technique. Finally, based on these feature extracted, the object will be detected and the detected objects will be tracked by utilising both forward and backward tracking technique. The proposed methodology proved to be more effective and accurate in object detection and tracking. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses various reports within the issue on topics including the impact of training data quality on the accuracy of image classification, the use of SPOT-5 multispectral and panchromatic image data in computing texture measures, and effective management strategies in fire-prone landscapes in Southeast Australia.

To evaluate the value of texture analysis of routine MRI image in peritumoral edema of differentiating diagnosis between glioblastoma (GBM) and primary brain lymphoma (PBL). The MRI imaging data of 22 patients with glioblastoma and 21 patients with PBL who were hospitalized in our hospital from January 2010 to October 2018 were selected. All the patients were pathologically diagnosed as glioblastoma or PBL, and MRI plain scan and enhanced examination were performed before operation. FireVoxel software was used to delineate the region of interest (ROI) on the most obvious level of peritumoral edema based on T1WI enhancement. Texture parameters were extracted and compared between glioblastoma and PBL. In the glioblastoma group, the inhomogeneity, kurtosis and entropy texture parameters were statistically different from those in the PBL group. The entropy parameter area under the curve (AUC) (0.903) was significantly better than the kurtosis parameter AUC (0.859) and the inhomogeneity parameter AUC (0.729). When the entropy parameter Cut-off point = 3.883, the sensitivity, specificity and accuracy of glioblastoma and PBL were 85.7, 86.4 and 86.0%, respectively, by differential diagnosis. Texture analysis of tumor peritumoral edema provided quantifiable information, which might be a new method for differentiating glioblastoma from PBL. [ABSTRACT FROM AUTHOR]

To overcome the shortcomings of time-consuming manual measurement methods of fabric density, a novel density measurement method of woven fabrics using image analysis technology via the relative total variation model based on the view of structure extraction from texture (RTV-SFT) is developed. Firstly, a fabric image acquisition system of multi-zoom portable digital microscope is constructed to obtain color dyed fabric images. Secondly, creatively from the perspective of analyzing periodic structure information of image texture information, a new structure extraction from texture algorithm, namely RTV-SFT is proposed to process fabric images to obtain images without non-periodic texture information aiming to achieve accurate yarn segmentation. Finally, the woven fabric density is calculated by effectively adopting the gray level projection method with the improved Gaussian filter algorithm. In the experiment, compared with manual method, the accuracy of the proposed density measurement method is above 93.3%. [ABSTRACT FROM AUTHOR]

We investigated whether novel parameters derived from a Dual-energy X-ray absorptiometry (DXA) scan might improve fracture risk estimation. In this study, we analysed hip DXA scans from 29 older adults with a history of fragility fracture and 90 non-fractured controls. Active shape models and active appearance models were used to allow a quantitative characterisation of the shape and gross structure of the proximal femur. We also performed image texture analysis applied to various regions of interest. Feature selection was used to determine which method, or combination of methods, was best to discriminate between the fracture and control groups. Texture features derived from Gabor filters in combination with total T-score provided better estimates of risk (AUC = 0.787) than the standard measures of areal bone mineral density or total T-score alone (AUC = 0.699 and 0.692, respectively). Estimates of risk were more accurate when the texture was measured on the whole femoral neck compared to other regions. The features extracted from the active models were weaker with poor classification performance (AUC < 0.570). This study shows that image texture based on Gabor filters can complement the standard measures to improve fracture risk estimation. [ABSTRACT FROM AUTHOR]

Carrot was impregnated with pigment solution by vacuum heat sealing (VS) and/or high hydrostatic pressure treatment (HHP; 100–600 MPa, 25°C, 5 min). Impregnation efficacy was evaluated as impregnation ratio (%) by image analysis, while damages of cell membrane and texture were quantified by electrical impedance spectroscopy and texture analysis, respectively. Combinations of VS and HHP exclusively achieved 100% impregnation. Meanwhile, the damages were comparable between the samples after VS and VS + 100 MPa treatment and aggravated by elevated levels of HHP. As for the texture, breaking stress (hardness) decreased after VS and VS + HHP (100–600 MPa) comparably. Meanwhile, breaking strain (deformability) significantly increased after VS + HHP (200–600 MPa), indicating further damage of strain texture as compared with that after VS and VS + 100 MPa treatments. It was indicated that impregnation of carrot was fully achieved by a combination of VS and 100 MPa treatment, while minimizing the cell membrane and texture damages. Difference in the impregnation efficacy between carrot and apple was discussed based on their image data. [ABSTRACT FROM AUTHOR]

The fractal dimension of a micrograph is a quantitative parameter of its geometric patterns' complexity, morphological irregularities and characteristics in their spatial arrangements and configurations. Material defects growing during fatigue process are described in terms of f r a c t a l s . The experimental assessment of fractal dimensions of the novel flaky electron fractographs comprising the 3D planar topography of branched secondary cracks with distinctively oriented striations' i s l a n d s and tearing ridges' n e t w o r k s on the 'fatigue crack propagation area' in three different Zr–Nb alloys have been carried out and correlated with their corresponding low cycle fatigue responses at different temperatures. Image texture analysis has also been implemented to understand the relative 'energy generation' of the hierarchical fatigue crack propagation during f a t i g u e − f r a c t u r e process of these materials as a function of test temperature. The detail invasive fractal character and chaos of these crack/striations' i s l a n d s and tearing ridge n e t w o r k morphologies after f a t i g u e − f r a c t u r e have been convincingly revealed. [ABSTRACT FROM AUTHOR]

Pollen analysis is still a manual task and an experienced pollen analyst may analyse two to four samples per day when a regular pollen sum of 300 pollen grains is used. Long continental pollen records (LCPRs) form a remarkable aspect of palynological research. The pollen analytical work Since 1988, efforts to analyse pollen grains automatically have been published regularly (Konert et al. [22]; Witte [34]; France et al. [9]; Marcos et al. [25]; Olsson et al. [28]) however, pollen analysis still is a manual procedure. [Extracted from the article]

Objectives:Orally disintegrating (mini)tablets (OD(M)Ts) are of interest in the field of pharmaceutics. Their orodispersible character is defined by the disintegration time, which is measured with a basket apparatus according to the European Pharmacopoeia. This method, however, lacks applicability for ODTs and especially ODMTs. New disintegration apparatuses have been described in literature, but a qualification to assess the applicability has not been described. A qualification procedure for two automated disintegration apparatuses, OD-mate and Hermes apparatus, is introduced. Methods:Aspects of the operational qualification as well as precision and accuracy regarding a performance qualification were evaluated for both apparatuses analog to the ICH guideline Q2. While the OQ study is performed separately for each apparatus, accuracy and precision were performed following the same protocol for both testers. Key findings:Small RSDs (16.9% OD-mate; 15.2% Hermes compared to 32.3% for the pharmacopeial method) were found despite very fast disintegration times (1.5 s for both apparatuses). By comparing these RSDs to practical examples, the authors propose threshold values for repeatability depending on the mean disintegration time. Obtained results from the qualification were used to assess the applicability of both apparatuses. [ABSTRACT FROM PUBLISHER]

Automatic detection of corrosion inhibition efficacy by processing the microscopic images of the sample surface by using image texture analysis is a very useful technique in the contemporary research field. The reported technique is less time consuming with the capability of pattern recognition via quantification with preferences of suitable software, coding and various numerical methods ultimately represents data (vide acquisition) more precisely and accurately at agiven condition. An optimal anisotropic diffusion-based pre-processing for image enhancement and run length statistics (RLS) based texture analysis for feature extraction are performed on the microscopic images of mild steel surface treated in 1 M HCl solution with different inhibitors viz., (E)-1-(2,4-dinitrophenyl)-2-(pyridin-4-yl)methylene)hydrazine (abbreviated as DHMP and represented as L1), (E)-1-(4-isocyanophenyl)-2-((pyridin-4-yl)methylene)hydrazine) (abbreviated as IMH and represented as L2) and (E)-1-(2,4-dinitrophenyl)-2-((pyridin-4-yl)methylene)phenol (abbreviated as DMP and represented as L3). Extracted normalized or relative features can be quantified the corrosion inhibition efficiency in an excellent way using multi-regression analysis having R-square values in the range of 0.997 to 0.999. (E)-1-(2,4-dinitrophenyl)-2-(pyridin-4-yl)methylene)hydrazine (abbreviated as DHMP and represented as L1), (E)-1-(4-isocyanophenyl)-2-((pyridin-4-yl)methylene)hydrazine) (abbreviated as IMH and represented as L2) and (E)-1-(2,4-dinitrophenyl)-2-((pyridin-4-yl)methylene)phenol (abbreviated as DMP and represented as L3) are used as three different corrosion inhibitors over the mild steel surface in presence of 1M HCl solution. Automatic detection of corrosion inhibition efficiency by processing the microscopic images of the targeted sample surface by via image texture analysis. An optimal anisotropic diffusion technique is adopted in this present study for image enhancement before processing the image. Run length statistics (RLS) based texture analysis has been incorporated in this present investigation for extracting run length features to detect the corrosion inhibition efficiency. After selecting the RLS-based features, a multivariable regression model is developed and correlations are set up with the features of conventional parameters of the different inhibitor's performance. [ABSTRACT FROM AUTHOR]

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

Image segmentation is one of the most important and challenging problems in image processing. The main purpose of image segmentation is to partition an image into a set of disjoint regions with uniform attributes. In this study, we propose an improved method for edge detection and image segmentation using fuzzy cellular automata. In the first stage, we introduce a new edge detection method based on fuzzy cellular automata, called thetexture histogram, and empirically demonstrate the efficiency of the proposed method and its robustness in denoising images. In the second stage, we propose an edge detection algorithm by considering the mean values of the edges matrix. In this algorithm, we use four fuzzy rules instead of 32 fuzzy rules reported earlier in the literature. In the third and final stage, we use the local edge in the edge detection stage to more accurately accomplish image segmentation. We demonstrate that the proposed method produces better output images in comparison with the separate segmentation and edge detection methods studied in the literature. In addition, we show that the method proposed in this study is more flexible and efficient when noise is added to an image. [ABSTRACT FROM AUTHOR]

The pavement distress information is essential for evaluating the pavement. Automatic pavement inspection systems can be so beneficial in providing needed information on pavement conditions. The bleeding is one of the asphalt pavement distresses and directly affects the pavement skid resistance and road safety. This study aims to develop an image-based system for the comprehensive evaluation of pavement bleeding. This evaluation consists of three main parts: bleeding occurrence detection, bleeding region segmentation, and severity-based classification of bleeding regions. For implementing the proposed system, deep learning-based model and transfer learning method are used in the detection part, and wavelet transform is the main process in the segmentation part. Also, gray-level co-occurrence matrix, wavelet transform, and signal to noise ratio are used to extract image texture-based features from various levels of the bleeding severity (low, mid, and high). Then a decision tree has been made based on extracted features for severity-based classification. The proposed system can indicate the bleeding index based on distress density and severity. Results show good performance in detection, segmentation, and severity-based classification parts, on the average of performance indices with 98%, 89%, and 93%, respectively. Therefore, the proposed system provides an efficient method for comprehensive pavement bleeding evaluation. [ABSTRACT FROM AUTHOR]

To quantify the homogeneity of fiber dispersion in short fiber-reinforced polymer composites, a method for image texture analysis of 3-dimensional X-ray micro computed tomography (µCT) images is presented in this work. The adaption of the method to the specific requirements of the composite material is accomplished using a statistical region merging approach. Subsequently, the method is applied for evaluating the homogeneity of specimens from an intermediate step of the long fiber thermoset injection molding process as well as molded parts. This new injection molding process enables the manufacturing of parts with a flexible combination of short and long glass fibers. By using a newly developed screw element based on the Maddock mixing element design, the material homogeneity of parts molded in the long fiber injection molding process is improved. [ABSTRACT FROM AUTHOR]

The main purpose of this work is to explore a reproducible way of replacing manual digitalizations of urban agglomerations using textural image processing from free high-resolution Google Earth images. Photo-interpretation from very high-resolution satellite images is a reliable method for extracting urban agglomerations, but it is extremely time consuming. As an alternative to manual digitalization, a numerical image analysis method based on mathematical morphology has been developed to detect textural patterns associated with built-up areas. Hundreds of urban agglomerations were extracted using this method and then compared to an open geodatabase. The results were better for humid regions, and omission errors were consistently larger than commission errors for the whole dataset, underlining the challenge of detecting highly scattered habitats with texture analyses. However, the method could provide an appropriate estimate of overall urbanization. This research highlights the weaknesses and the strengths of the method compared to manual digitalization, illuminating paths for future improvement of alternative procedures to manual digitalization. [ABSTRACT FROM AUTHOR]

It is an undisputable fact that surface texture has a profound influence on the functional behavior of products. A method is proposed for manufacturing an anti-icing surface on silicone rubber composite material. Using a multi-function laser engraving machine, cylindrical micro-textures with different size and depth parameters are processed on the umbrella skirt surface of composite insulator samples. The optimal dimensional parameter of the surface micro-texture was determined by the best superhydrophobic property. By analyzing the freezing time of droplets, the influence of texture depth parameters on the anti-icing performance of the silicone rubber surface was investigated, which clearly confirmed that the contact angle affected anti-icing performance. Studying on adhesion of the ice on the sample surface and changing of the water contact angle after ice coating demonstrated the influence of texture-depth parameter on the deicing performance of silicone rubber surface. Thus, the characteristics and optimality of the texture specimen were obtained. [ABSTRACT FROM AUTHOR]

A finite element microstructure model with permeability tensors that considers crystallographic texture and grain size based on magnetic domain theory has been developed for the evaluation of magnetic anisotropy in polycrystalline steels. The model has proved capable of capturing the crystallographic texture, the grain size and the vector induction effects on the effective permeability behaviours for typical textures in steels. The predicted magnetic properties as a function of the magnetic field direction enables a quantitative characterisation of the magnetic anisotropy. The predicted effective permeability maps can serve as a visual indication of the crystallographic texture from magnetic values. These features have been experimentally validated against a commercial grain oriented electrical steel featuring strong texture and magnetic anisotropy. [ABSTRACT FROM AUTHOR]

Remotely sensed high spatio-temporal resolution panchromatic images have been extensively used globally to visually detect and interpret changes in landscape components, create land cover maps via the on-screen manual digitization, and to pan-sharpen multi-spectral images among other uses. Despite this attractive array of uses, lack of distinct spectral signatures for panchromatic images from surface elements, e.g. landscape cover types, creates a drawback in their exploitation during any automated classification process, hence limiting their use in the field of remote sensing for land use/land cover change studies. Moreover, the complexities of some panchromatic data (e.g. CORONA) on the one hand, and the traditional texture computation approach on the other hand present additional hurdles in utilizing panchromatic images. This contribution looks at the possibility of exploiting panchromatic images (e.g. Pleiades and historical CORONA products) for remote sensing applications by (i) proposing a new approach that optimizes and generates new multi-layer datasets from panchromatic images that could be useful, e.g. in image classification analysis, (ii)exploiting the combinatorial texture approach to enhance the products generated by the framework in (i) above, and (iii) assessing the capability of the proposed method to handle complex datasets exemplified, e.g. by CORONA. To evaluate the approach, Kurdistan, Iran and Syria regions are selected for study employing the maximum likelihood classification (MLC) scheme. The MLC results indicate an increase in overall accuracy and Kappa coefficient by 32% and 0.42 (compared to raw CORONA image), and 21% and 0.28 (compared to raw Pleiades image). For Iran and Syria, compared to the raw CORONA image, the MLC results show increase by 35% and 0.47, and 42% and 0.56, respectively. Furthermore, based on the results of the accuracy assessment that show an overall accuracy of 85% and Kappa coefficient of 0.80 for Kurdistan, 94% and 0.92 for Iran, and 96% and 0.95 for Syria, the proposed method can be said to have the potential of handling complex panchromatic datasets such as CORONA. [ABSTRACT FROM AUTHOR]

Numerical models for barrel-shaped rings with four textures were established using MATLAB and the corresponding specimens were fabricated by laser surface texturing. The performance was compared both theoretically and experimentally in order to investigate the effects of geometrical characteristics. The results of the theoretical analysis indicated that a microdimple texture on both sides of the ring surface was a suitable option to improve the lubricating property due to the "collective" effect, thus reducing friction of the piston ring. This result was confirmed experimentally. The microdimple texture at the crowning of the ring face weakened the hydrodynamic pressure effect and increased friction, as demonstrated by both theoretical and experimental results. [ABSTRACT FROM AUTHOR]

To detect chronic kidney disease (CKD) at earlier stages, diagnosis through non-invasive ultrasonographic imaging techniques provides an auxiliary clinical approach for at-risk CKD patients. We have established a detection method based on imaging processing techniques and machine learning approaches for the diagnosis of different CKD stages. Decisive area-proportional and textural features and support-vector-machine techniques were applied for efficient and effective analyses. Several clustered collections of CKD patients were evaluated and compared according to the estimated glomerular filtration rates. Based on the findings of evolving changes from ultrasound images, the proposed approach could be used as complementary evidences to help differentiate between different clinical diagnoses. [ABSTRACT FROM AUTHOR]

The study of dental wear was first used years ago to infer the palaeoecology of fossil mammals and in particular their diet. Results depend predominantly on the scale of the analysis used. Analyses of dental macrowear, mesowear or microwear do not provide the same type of dietary information, be it about the seasonal, annual or lifetime diet. This contribution focuses on emblematic species, cave bears (Ursidae), in particular Ursus spelaeus spelaeus. Methods used by previous researchers to infer their dietary preferences and thus their palaeoecology are reviewed and compared. This review is complemented by an analysis of several specimens of cave bears from the Goyet cave in Belgium, using dental microwear texture analysis (DMTA), a methodology widely applied for reconstructing palaeodiets. Three main conclusions are drawn here: (1) DMTA is the method that provides the most precise palaeobiological inferences; (2) during the pre-dormancy period, cave bears show dietary flexibility; (3) dental wear alone might be not sufficient to provide a complete reconstruction of the cave bear palaeodiet. [ABSTRACT FROM AUTHOR]

In this study, we propose a new method for cloud and snow discrimination, which is based on spectral, texture, and shape features and combined with support vector machine multi-classification strategy. A new feature called curvature histogram is designed to describe edge shape. First, the region of interest test is applied to extract cloud and snow area. Then, the extracted area is combined with the segmentation results using the mean shift algorithm to obtain the object of interest while calculating the feature values. The complexity of surfaces in the cloud and snow area is classified into four types, namely, thick cloud, thin cloud, snow, and snow-covered land, such that six kinds of classifiers are obtained by designing a classifier between every two categories. Through six classifiers and calculating the sum of confidence coefficients for each category, every superpixel is classified into the class with the highest confidence coefficient and a rough cloud and snow mask is obtained. Finally, the GrabCut algorithm is applied to optimize the classification results at the pixel level. The experiments on the images of China’s GF-1 satellite, indicate that the proposed method is effective for cloud and snow discrimination on multispectral high-resolution satellites images in mountainous area. [ABSTRACT FROM AUTHOR]

In this study, we integrate confidence into efficient large-scale stereo (ELAS) matching to produce a more accurate approach to binocular stereo for high-resolution image matching. Elas ensures good performance in the presence of poorly textured and slanted surfaces, but one of its deficiencies is its unsatisfactory ability to capture disparity discontinuities. Our formulation explicitly models the effects of confidence as a likelihood term in a principled manner using the Bayes rule. Because it is an iterative method, we associate each point with a variable confidence value and update this value based on a given confidence updating rule. Meanwhile, complementary support points are selected from stable points whose confidence value exceeds a predefined threshold, which differs from ELAS, whose support points are matched in advance and kept unchanged in the subsequent process. Confidence also plays a vital role in avoiding expensive computation, and the adjustment of support points makes disparity estimation more flexible. Quantitative evaluation demonstrates the effectiveness and efficiency of the proposed formulation in improving the accuracy of disparity estimation. [ABSTRACT FROM AUTHOR]

Purpose: Hyperthermia and radiation have the ability to induce structural and morphological changes on both macroscopic and microscopic level. Normal and damage cells have a different texture but may be perceived by human eye, as having the same texture. Materials and methods: To explore the potential of texture analysis based on run-length matrix, a total of 32 sphere images for each group and treatment regime were used in this study. Cells were subjected to the treatment with different doses of 6 MeV electron radiation (0 2, 4 and 6 Gy), hyperthermia (at 43° C in 0, 30, 60 and 90 min) and radiation + hyperthermia (at 43 °C in 30 min with 2, 4 and 6 Gy dose), respectively. Twenty run-length matrix (RLM) features were extracted as descriptors for each selected region of interest for texture analysis. Linear discriminant analysis was employed to transform raw data to lower-dimensional spaces and increase discriminative power. Results: The features were classified by the first nearest neighbor classifier. RLM features represented the best performance with sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of 100% between 0 and 6 Gy radiation, 0 and 6 Gy radiation + hyperthermia, 0 and 90 min and 30 and 90 min hyperthermia groups. The area under receiver operating characteristic curve was 1 for these groups. Conclusion: RLM features have a high potential to characterize cell changes during different treatment regimes. [ABSTRACT FROM AUTHOR]

The representation and analysis of building patterns are critical for characterizing urban scenes and making decisions in urban planning. The evaluation of building patterns is a difficult spatial analysis problem that exhibits properties of symbolization, homogeneity and regularity. Open issues in this field include the development of approaches for representing building patterns and vector-based methods for computing various pattern metrics. In the image analysis domain, there are many methods for pattern recognition (e.g., texture analysis), but there are few corresponding solutions for vector data. The aim of this research is to develop several building pattern metrics and offer a texton co-occurrence matrix (TCM)-based method to quantitatively evaluate the features of building patterns. The procedure first constructs a spatial field based on a Delaunay triangulation skeleton to partition a set of buildings into a set of tessellation cells. The tessellations of building clusters have a similar structure as image representations, in that each cell corresponds to an image pixel. We then use the texton analysis to establish a matrix to describe the tessellation structure, including the neighboring relationships and individual attribute information. Finally, a set of feature descriptors is obtained from the TCM to capture the texture-related information of building groups. Through experiments on building pattern analysis and spatial queries, we show that the results of TCM-based evaluation of building patterns are consistent with those of human cognition. [ABSTRACT FROM PUBLISHER]

Slum detection from satellite imagery is challenging due to the variability in slum types and definitions. This research aimed at developing a method for slum detection based on the morphology of the built environment. The method consists of segmentation followed by hierarchical classification using object-oriented image analysis and integrating expert knowledge in the form of a local slum ontology. Results show that textural feature contrast derived from a grey-level co-occurrence matrix was useful for delineating segments of slum areas or parts thereof. Spatial metrics such as the size of segments and proportions of vegetation and built-up were used for slum detection. The percentage of agreement between the reference layer and slum classification was 60 percent. This is lower than the accuracy achieved for land cover classification (80.8 percent), due to large variations. We conclude that the method produces useful results and has potential for successful application in contexts with similar morphology. [ABSTRACT FROM AUTHOR]

A family of 26 non-parametric texture descriptors based on Histograms of Equivalent Patterns (HEP) has been tested, many of them for the first time in remote sensing applications, to improve urban classification through object-based image analysis of GeoEye-1 imagery. These HEP descriptors have been compared to the widely known texture measures derived from the gray-level co-occurrence matrix (GLCM). All the five finally selected HEP descriptors (Local Binary Patterns, Improved Local Binary Patterns, Binary Gradient Contours and two different combinations of Completed Local Binary Patterns) performed faster in terms of execution time and yielded significantly better accuracy figures than GLCM features. Moreover, the HEP texture descriptors provided additional information to the basic spectral features from the GeoEye-1's bands (R, G, B, NIR, PAN) significantly improving overall accuracy values by around 3%. Conversely, and in statistic terms, strategies involving GLCM texture derivatives did not improve the classification accuracy achieved from only the spectral information. Lastly, both approaches (HEP and GLCM) showed similar behavior with regard to the training set size applied. [ABSTRACT FROM AUTHOR]

A close relationship has been found between the 3D collagen structure and physiological condition of articular cartilage (AC). Studying the 3D collagen network in AC offers a way to determine the condition of the cartilage. However, traditional qualitative studies are time consuming and subjective. This study aims to develop a computer vision-based classifier to automatically determine the condition of AC tissue based on the structural characteristics of the collagen network. Texture analysis was applied to quantitatively characterise the 3D collagen structure innormal(International Cartilage Repair Society, ICRS, grade 0),aged(ICRS grade 1) andosteoarthriticcartilages (ICRS grade 2). Principle component techniques and linear discriminant analysis were then used to classify the microstructural characteristics of the 3D collagen meshwork and the condition of the AC. The 3D collagen meshwork in the three physiological condition groups displayed distinctive characteristics. Texture analysis indicated a significant difference in the mean texture parameters of the 3D collagen network between groups. The principle component and linear discriminant analysis of the texture data allowed for the development of a classifier for identifying the physiological status of the AC with an expected prediction error of 4.23%. An automatic image analysis classifier has been developed to predict the physiological condition of AC (from ICRS grade 0 to 2) based on texture data from the 3D collagen network in the tissue. [ABSTRACT FROM AUTHOR]

In the current research work, rivastigmine (RV)-loaded in situ gelling nanostructured lipid carriers (NLCs) were developed for nose to brain delivery. NLCs were fabricated by ethanol injection method using glyceryl monosterate, Capmul MCM C8, Lecithin and Tween 80. NLCs showed average particle size of 123.2 ± 2.3 nm with entrapment efficiency of 68.34 ± 3.4%. DSC, XRD and IR studies showed complete amorphization and incorporation of the drug into nanoparticles. NLCs were incorporated into an in situ gelling system using 0.8% gellan gum and 15% Lutrol F 127. RV in situ gel showed excellent elasticity, rheology, mucoadhesion and adhesiveness to facilitate its adhesion to the upper nasal mucosa. NLC-based in situ gel showed a 2-fold increase in nasal permeation of the drug over plain RV solution, in situ gelling NLCs showed a 3-fold increase in enzyme inhibition efficacy. [ABSTRACT FROM AUTHOR]