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Waterlogging has recently occurred in cities across China, causing serious economic and property losses. It is imperative to assess the susceptibility of cities to waterlogging, especially those in urban areas, to reduce the damages caused by waterlogging. Artificial Neural Network algorithms have been used to assess the hazards of waterlogging because of their simplicity, objectivity and efficiency. Considering the neighborhood effects, this research proposes an integrated Multiple Geograpical Units Convolutional Neural Network (MGCNN) model for assessing the waterlogging risk in the primarily urban area of Guangzhou, China. Ten spatial factors were considered to be added to the MGCNN, which are DEM, Slope, Std, NDVI, NDBI, MNDWI, Entropy, Contrast, Correlation and Energy based on a Gray-level Co-occurrence Matrix (GLCM). The results show that 1) the texture features of remote sensing image plays an important role in the identification of urban waterlogging area through significance analysis of the ten impact factors; 2) the accuracy rate of the MGCNN trained with the input data of the four risk levels is higher than 0.88, while the maximum accuracy rate of the two risk levels is only about 0.6; 3) the MGCNN has a lower cross-entropy loss value of 0.82 than the SGCNN of 1.13 after 20,000 iterations of training; 4) The Area Under the Curve (AUC) value of the MGCNN was about 0.95, highest among the MGCNN,SGCNN, SVM-BRF and SVM-Poly models; 5) Extremely high risk areas for waterlogging were mainly found in low-lying areas with a high density of urban buildings, including the Yuexiu district, the northern Liwan district, the central and southern sections of the Tianhe district, and the western Haizhu district. Therefore, fully considering neighborhood effects of the surrounding geographical units, the constructed MGCNN model improves not only the accuracy but also the interpretability of the black box. •Spatial evaluation of urban waterlogging risk can be beneficial in creating strategies for urban waterlogging mitigation. •A spatial framework integrating CNN with GIS was introduced to assess urban waterlogging risk. •Compared with two-level data, the MGCNN model trained using four-level data can further improve the accuracy of evaluation. •Image texture based on GLCM has a positive impact on assessment results. •Considering spatial correlation, and the accuracy of the MGCNN was effectively improved. [ABSTRACT FROM AUTHOR]

A noniterative fractional-order (FO) two-dimensional (2-D) adaptive filtering mechanism is proposed in this article. Quartiles-based adaptive textural segmentation is employed for the calculation of texture-dependent FO. Statistically, the quartiles/quintiles/quantiles are cut points for dividing the range of a probability distribution into continuous intervals with equal probabilities. For the purpose of texture-based isolation of the spatial regions, a 2-D textural map is framed by evaluating spatial entropy by considering a pixel-wise local circular neighborhood. A novel end-to-end framework is proposed for FO texture-dependent image sharpening in an independent manner without influencing the other classes of textural regions. A novel inclusion of texture-wise adaptive gamma correction is also proposed in this article by drafting a mechanism where different kinds of textural regions can be separately processed in an independent manner. A novel quintiles-based multiscale Retinex (MSR) inspired approach for reflectance computation is coined in this article for suppressing environmental artifacts and unbalanced/nonuniform illumination. In this context, various scales required for MSR are themselves computed through quartiles-based intensity levels. The proposed model is highly modular. So, it can also be pipelined in a parallel manner, along with any well-established state-of-the-art contrast enhancement approach. Also, this approach is noniterative and highly robust. It can be proposed as an add-on for several possible (as well as pre-existing) image processing procedures. Rigorous comparative evaluations are performed so that the excellence of the proposed approach can be underlined. [ABSTRACT FROM AUTHOR]

Creep deformation of human vertebrae accumulates under physiological levels of load and is understood to contribute to the progression toward clinically observable vertebral fracture. However, little information is available in terms of clinically measurable predictors of creep behavior in human vertebrae. In this study, creep tests were performed on 22 human cadaveric T12 vertebrae (13 male, 9 female; age 41–90). Areal and volumetric bone density parameters were measured from the same specimens using dual x-ray absorptiometry and high resolution computed tomography. Image textural analyses (which probe the organization of image intensities within the cancellous bone in low resolution clinical imaging) were performed using digital tomosynthesis (DTS) images. Multiple regression models were constructed to examine the relationship between creep properties and bone density and DTS image textural parameters. For the standard clinical imaging configuration, models including DTS derived image textural parameters alone were generally more explanatory (adjusted R2: 0.14–0.68) than those with bone density parameters forced in the models (adjusted R2: 0.17–0.61). Metrics of textural heterogeneity and anisotropy presented as the most explanatory imaging markers for creep deformation and recovery from creep. These metrics of image texture may help provide, independent from bone mass, important clinically measurable indicators of the time dependent deformation of human vertebrae. [ABSTRACT FROM AUTHOR]

Multifractal analysis was applied to evidence spatial properties, in particular scaling behaviour, of Sentinel-2 surface reflectances and optical indexes acquired over the Southwest region of France. Considering the significant impact of clouds on the spatial properties of optical products, no study to our knowledge has focused on the multifractal analysis of a series of images. Images with minimal cloud cover were identified for the whole year 2016 based on the MACCS-ATCOR Joint Algorithm (MAJA) cloud and shadow detection process. This allowed us to access the dynamics of the scaling properties of surface reflectances, contributing to demonstrate the potential of multifractals for spatial characterization. Different scaling regimes, separated by significant scale breaks, were evident in these data. Through simulations from the Universal Multifractal model, we could explain the scaling properties and especially the position of scale breaks observed in the images studied by two types of phenomena. First, a transition scale located between hundreds of meters and one kilometre was detected on power spectra, structure functions and statistical moments. This specific scale was related to the average size of the agricultural plots of the studied region, revealing the direct link between the scaling behaviour observed and the physical properties of the surface. Second, an instrumental phenomenon was detected on the power spectra, resulting in a systematic transition scale observed near the resolution of the sensor and revealing the impact of the acquisition system on the high frequencies of optical images. In this research work, theoretical and/or empirical validation arguments were provided as to the origin of the scales detected, contributing to demonstrate the value of multifractals to extract image texture from surface reflectance products. • Particular scale breaks were detected on Sentinel-2/MAJA products. • Time variations in scaling regimes were noticed in series of surface reflectances. • The large-scale break was related to the landscape spatial structure. • The fine-scale break was explained by the effect of the optical acquisition system. [ABSTRACT FROM AUTHOR]

Limited-view Computed Tomography (CT) can be used to efficaciously reduce radiation dose in clinical diagnosis, it is also adopted when encountering inevitable mechanical and physical limitation in industrial inspection. Nevertheless, limited-view CT leads to severe artifacts in its imaging, which turns out to be a major issue in the low dose protocol. Thus, how to exploit the limited prior information to obtain high-quality CT images becomes a crucial issue. We notice that almost all existing methods solely focus on a single CT image while neglecting the solid fact that, the scanned objects are always highly spatially correlated. Consequently, there lies bountiful spatial information between these acquired consecutive CT images, which is still largely left to be exploited. In this paper, we propose a novel hybrid-domain structure composed of fully convolutional networks that groundbreakingly explores the three-dimensional neighborhood and works in a "coarse-to-fine" manner. We first conduct data completion in the Radon domain, and transform the obtained full-view Radon data into images through FBP. Subsequently, we employ the spatial correlation between continuous CT images to productively restore them and then refine the image texture to finally receive the ideal high-quality CT images, achieving PSNR of 40.209 and SSIM of 0.943. Besides, unlike other current limited-view CT reconstruction methods, we adopt FBP (and implement it on GPUs) instead of SART-TV to significantly accelerate the overall procedure and realize it in an end-to-end manner. [ABSTRACT FROM AUTHOR]

Simple Summary: Overloading of the horse's thoracolumbar region is a serious problem mainly affecting sport and school horses during their daily under-saddle work. As the human population becomes heavier, the effect of rider bodyweight on equine welfare and performance requires further investigation. This study used infrared thermography to assess the effect of rider:horse bodyweight ratio on the horse's thoracolumbar region by introducing advanced digital image processing. Twelve horses during regular work were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images of the back region were taken before and after standard exercise and underwent conventional analysis and texture analysis where the thermal images were separated into red, green, and blue components. Four areas of the horse's thoracolumbar region were annotated to represent the withers area, the thoracic spine area, and the left and right areas of back muscles. Among 372 returned features, 75 texture features differed between bodyweight ratio groups, whereas the conventional thermal features did not. Contrary to conventional thermal features, the consistent measurable differences in texture features were evidenced predominantly in the red component of thermal images when the texture heterogeneity measures, such as InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, were considered. Appropriate matching of rider–horse sizes is becoming an increasingly important issue of riding horses' care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body's surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10–12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered. [ABSTRACT FROM AUTHOR]

Facial recognition systems are widely used in various daily applications due to their high accuracy rate and ease of use. Unfortunately, their performance drops drastically when encountering spoofing attacks. In this paper, a novel pipeline for face spoofing detection based on the extraction of Heterogeneous Auto-Similarities of Characteristics (HASC) descriptor from the HSV and YCbCr color spaces is proposed. To detect print and replay spoofing attacks, the HASC descriptor encodes linear and non-linear dependencies between low-level dense features of the color face image using covariance and information-theoretic measures. The combination of covariance and information-theoretic measures makes HASC an efficient descriptor to explore how the features are interrelated in real and fake faces. The HASC texture descriptions are extracted separately from each color channel of the HSV and YCbCr color spaces, and the resulting feature vectors are concatenated into an extended feature vector to better discriminate between real and fake faces. Extensive experiments are conducted on publicly available databases namely, Replay-Attack, CASIA FASD, and MSU MFSD. The obtained results demonstrate that while being of low dimension, the HASC descriptor when fed into a binary Support Vector Machine (SVM) classifier, achieves competitive results compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) images are inherently degraded by speckle noise caused by coherent imaging, which may affect the performance of the subsequent image analysis task. To resolve this problem, this article proposes an integrated SAR image despeckling model based on dictionary learning and multi-weighted sparse coding. First, the dictionary is trained by groups composed of similar image patches, which have the same structural features. An effective orthogonal dictionary with high sparse representation ability is realized by introducing a properly tight frame. Furthermore, the data-fidelity term and regularization terms are constrained by weighting factors. The weighted sparse representation model not only fully utilizes the interblock relevance but also reflects the importance of various structural groups in despeckling processing. The proposed model is implemented with fast and effective solving steps that simultaneously perform orthogonal dictionary learning, weight parameter updating, sparse coding, and image reconstruction. The solving steps are designed using the alternative minimization method. Finally, the speckles are further suppressed by iterative regularization methods. In a comparison study with existing methods, our method demonstrated state-of-the-art performance in suppressing speckle noise and protecting the image texture details. [ABSTRACT FROM AUTHOR]

In this paper, we propose an unsupervised textural-intensity-based natural image quality evaluator (TI-NIQE) by modelling the texture, structure and naturalness of an image. In detail, an effective quality-aware feature named as textural intensity (TI) is proposed in this paper to detect image texture. The image structure is captured by the distribution of gradients and basis images. The naturalness is characterized through the distributions of the locally mean subtracted and contrast normalized (MSCN) coefficients and the products of pairs of the adjacent MSCN coefficients. Furthermore, a new application pattern of image quality assessment (IQA) measures is proposed by taking the quality scores as the essential input of the recognition model. Using statistics of video quality scores computed by TI-NIQE as input features, an automatic IQA-based visual recognition model is proposed for the condition recognition in rotary kiln. Extensive experiments on benchmark datasets demonstrate that TI-NIQE shows better performance both in accuracy and computational complexity than other state-of-the-art unsupervised IQA methods, and experimental results on real-world data show that the recognition model has high prediction accuracy for condition recognition in rotary kiln. • Proposed an effective image textural intensity detection method. • Proposed a low computational complexity yet has high prediction accuracy unsupervised BIQA method. • Realized the quality evaluation of the flame image. • Proposed a robust sintering condition recognition model in rotary kiln. [ABSTRACT FROM AUTHOR]

A superpixel segmentation algorithm called multi-density peaks clustering (MDPC) is proposed. By selecting a sufficient number of local density maximum pixels from the image as cluster centers to depict the image texture, the boundary of the object can be captured very accurately. The algorithm framework of MDPC is divided into three steps. First, the local density of pixel is defined, and the local density maximum pixels are calculated. Then, the local density maximum pixels are used as cluster centers, and the global optimal search, which is based on the path-to-point idea, is used to complete the clustering of the remaining non-cluster center pixels to realize the initial segmentation. Finally, superpixels are obtained by merging the initial segments according to the size of the segments and the distance between adjacent segments. In quantitative comparisons, MDPC is compared with 13 state-of-the-art superpixel segmentation algorithms in three image segmentation datasets. The experimental results show that MDPC achieves better performance in terms of boundary recall, boundary precision, achievable segmentation accuracy, undersegmentation error, and explained variation. And the qualitative comparisons show that the proposed algorithm has obvious advantages over other superpixel segmentation algorithms in image detail description and boundary adherence. Finally, the practicability and stability of MDPC are further demonstrated by the application of image segmentation. The source code of MDPC will be available at https://github.com/zhaojianaaa. [ABSTRACT FROM AUTHOR]

• Our method takes into account the VOIs of the TCs, without the need to divide them into slices, i.e, we analyze the entire volume. • We propose eight image texture descriptors, which do not require parameterization and not need to resize images. • We have developed a scalable method, since it can be easily used in 2D or 3D images, without restrictions regarding the quantization of images. • Our descriptors can achieve results, as promising as deep networks, in some cases, with superior results. • Our descriptors, too, do not need large amounts of images to achieve good results. COVID-19 is an infectious disease caused by a newly discovered type of coronavirus called SARS-CoV-2. Since the discovery of this disease in late 2019, COVID-19 has become a worldwide concern, mainly due to its high degree of contagion. As of April 2021, the number of confirmed cases of COVID-19 reported to the World Health Organization has already exceeded 135 million worldwide, while the number of deaths exceeds 2.9 million. Due to the impacts of the disease, efforts in the literature have intensified in terms of studying approaches aiming to detect COVID-19, with a focus on supporting and facilitating the process of disease diagnosis. This work proposes the application of texture descriptors based on phylogenetic relationships between species to characterize segmented CT volumes, and the subsequent classification of regions into COVID-19, solid lesion or healthy tissue. To evaluate our method, we use images from three different datasets. The results are promising, with an accuracy of 99.93%, a recall of 99.93%, a precision of 99.93%, an F1-score of 99.93%, and an AUC of 0.997. We present a robust, simple, and efficient method that can be easily applied to 2D and/or 3D images without limitations on their dimensionality. [ABSTRACT FROM AUTHOR]

• An automated, machine-learning based, material-testing fracture characterization process is elaborated. • Image-based, highly-significant texture features are identified and classified. • Neural network, high accuracy material-testing fracture classifiers are trained. • The applicability of the methodology in different ductile fracture experiments is illustrated. Correctly determining the onset of fracture is crucial when performing mechanical experiments. Commonly carried out by visual inspection, here an image-based machine learning approach is proposed to classify cracked and un-cracked specimens. It yields the potential to objectify and automate crack detection, thereby removing sources of uncertainty and error from the post-processing of experiments. More than 30'000 speckle-pattern images obtained from 77 experiments on three specimen geometries are evaluated. They comprise uniaxial tension, notched tension as well as axisymmetric V-bending experiments. Statistical texture features are extracted from all images. They include both first-order (variance, skewness, kurtosis) and higher-order statistical texture features, i.e. Haralick features. The discriminatory power of the texture information is evaluated based on the Fisher's Discriminant Ratio and feature correlations are identified and quantified. Image texture feature subsets of high discriminatory power are used to parse neural network architectures of different complexities from simple perceptron to feed-forward and cascade neural networks. It is found that a small subset of the investigated texture features is highly significant for all experiments. Using this feature subset in conjunction with multi-layer, non-linear and low complexity feed-forward network architectures classification accuracies in the order of 99% are obtained. At the same time, it is shown that linear classifiers are not sufficient to robustly distinguish the state of the specimens, even when high discriminatory power features are used. Graphical Abstract: [Display omitted] [ABSTRACT FROM AUTHOR]

• A novel covariogram regularization is proposed to extract image texture details. • The elementary forms of two kinematic measure formulas are obtained about a regular hexagon. • Experiments show that the proposed model is capable of achieving the accurate restoration result. Infrared images often suffer from the problems of blurry and random noise. To address those issues, we firstly proposed the kinematic measure formula of a convex body that contains a fixed segment through covariogram in n -dimensional Euclidean space. Then, the proposed covariogram expression is considered as the regularization condition to restore the resolution of infrared images. Furthermore, a case of a regular hexagon is illustrated in detail. We deserve the explicit expression for the covariogram, the kinematic measure of a regular hexagon that contains a fixed segment. Experimental results demonstrate that the proposed CRIIR model achieves better performance compared to the state-of-the-art methods. The source implementation code will provide upon request. [ABSTRACT FROM AUTHOR]

Rapid and accurate extraction of water bodies from high-spatial-resolution remote sensing images is of great value for water resource management, water quality monitoring and natural disaster emergency response. For traditional water body extraction methods, it is difficult to select image texture and features, the shadows of buildings and other ground objects are in the same spectrum as water bodies, the existing deep convolutional neural network is difficult to train, the consumption of computing resources is large, and the methods cannot meet real-time requirements. In this paper, a water body extraction method based on lightweight MobileNetV2 is proposed and applied to multisensor high-resolution remote sensing images, such as GF-2, WorldView-2 and UAV orthoimages. This method was validated in two typical complex geographical scenes: water bodies for farmland irrigation, which have a broken shape and long and narrow area and are surrounded by many buildings in towns and villages; and water bodies in mountainous areas, which have undulating topography, vegetation coverage and mountain shadows all over. The results were compared with those of the support vector machine, random forest and U-Net models and also verified by generalization tests and the influence of spatial resolution changes. First, the results show that the F1-score and Kappa coefficients of the MobileNetV2 model extracting water bodies from three different high-resolution images were 0.75 and 0.72 for GF-2, 0.86 and 0.85 for Worldview-2 and 0.98 and 0.98 for UAV, respectively, which are higher than those of traditional machine learning models and U-Net. Second, the training time, number of parameters and calculation amount of the MobileNetV2 model were much lower than those of the U-Net model, which greatly improves the water body extraction efficiency. Third, in other more complex surface areas, the MobileNetV2 model still maintained relatively high accuracy of water body extraction. Finally, we tested the effects of multisensor models and found that training with lower and higher spatial resolution images combined can be beneficial, but that using just lower resolution imagery is ineffective. This study provides a reference for the efficient automation of water body classification and extraction under complex geographical environment conditions and can be extended to water resource investigation, management and planning. [ABSTRACT FROM AUTHOR]

Image texture description and analysis technology are the basis of many practical applications in pattern recognition. This paper presents a novel and simple, yet powerful method, namely multiple channels local binary pattern (MCLBP), which is the natural extension and development of local binary pattern (LBP) algorithm for color texture representation and classification. MCLBP combines single-channel texture characteristics with multi-channel color information, which reflects the correlations and dependency among different channels. Furthermore, we decompose local color differences into color-difference signs and color-difference magnitudes and MCLBP is extended to MCLBP+M. Then, the resulted image descriptor is a histogram representation, which fuses rich features including color difference sign and color difference magnitude. Comprehensive experiments conducted on five benchmark databases, including Outex, KTH-TIPS, CUReT, STex and KTH-TIPS2-b clearly demonstrate that our proposed method outperforms most of the existed color texture features in terms of classification accuracy. Particularly, our method achieves the best classification performance in CUReT and STex databases. • A multi-channels LBP encodes inner- and inter-channel features of color images. • MCLBP extracts local features from three channels at once. • MCLBP+M characterizes both color difference signs and color difference magnitudes. • Comprehensive experiments are conducted on five benchmark datasets. [ABSTRACT FROM AUTHOR]

To effectively detect the surface cracks of subway tunnels, an automatic tunnel crack detection system based on machine vision is presented. Aiming at the problems of environmental complexity and low contrast in subway tunnels, the image texture feature is first enhanced by the methods of frequency domain filtering and spatial differencing. Then, depending on the characteristics of the tunnel cracks in question, the crack propagation method is used to extract the complete cracks. Finally, broken cracks are connected during processing, and the method of combining projection and threshold is used to determine the crack types. At the same time, characteristics such as the length, width, and area of the cracks are obtained. The experimental results show that the presented methods can effectively extract complete cracks in complex tunnel environments. The identification error of tunnel crack parameters meets the actual engineering requirements. [ABSTRACT FROM AUTHOR]

• Propose a novel artificial intelligence model to recognize concrete spall. • Combine metaheuristic, image processing, and machine learning approaches. • Kapur's entropy criterion is employed for image segmentation. • Image texture analysis is used as feature extractor. • Metaheusitic optimized LogitBoost ensemble is used for pattern classification. This paper presents a novel artificial intelligence model to automatically recognize concrete spall appearing on building components. The model is constructed by integrating a metaheuristic optimization algorithm, advanced image processing techniques, and a powerful machine learning-based inference model. Kapur's entropy based image segmentation, statistical measurements of image color, gray level co-occurrence matrices, and local ternary pattern are used to extract numerical features presenting concrete surfaces on spall and non-spall samples. Subsequently, a LogitBoost based ensemble framework of classification and regression tree (CART) models (denoted as LBT) is employed to construct a decision boundary capable of recognizing spall/non-spall image samples. Moreover, in order to enhance the performance of the LogitBoost based ensemble framework, forensic-based investigation (FBI) metaheuristic is utilized to determine the most suitable set of the framework's hyper-parameters including the learning rate (μ), the learning cycle (L c), the minimum number of leaves (L min), and the maximum number of splits (S max). A data set including 486 image samples has been collected from field surveys at high-rise buildings in Da Nang city (Vietnam) to train and verify the proposed FBI optimized LBT model (denoted as F-LBT). Experimental results supported by statistical tests point out that the F-LBT is a capable method for concrete spall detection with a classification accuracy rate = 88.3%, precision = 0.889, recall = 0.874, F1 score = 0.881, and negative predictive value = 0.874. Hence, the proposed hybrid approach is a promising tool to support building maintenance agencies in the task of periodic structural inspection. [ABSTRACT FROM AUTHOR]

Image representation plays a vital role in the realisation of Content-Based Image Retrieval (CBIR) system. The representation is performed because pixel-by-pixel matching for image retrieval is impracticable as a result of the rigid nature of such an approach. In CBIR therefore, colour, shape and texture and other visual features are used to represent images for effective retrieval task. Among these visual features, the colour and texture are pretty remarkable in defining the content of the image. However, combining these features does not necessarily guarantee better retrieval accuracy due to image transformations such rotation, scaling, and translation that an image would have gone through. More so, concerns about feature vector representation taking ample memory space affect the running time of the retrieval task. To address these problems, we propose a new colour scheme called Stack Colour Histogram (SCH) which inherently extracts colour and neighbourhood information into a descriptor for indexing images. SCH performs recurrent mean filtering of the image to be indexed. The recurrent blurring in this proposed method works by repeatedly filtering (transforming) the image. The output of a transformation serves as the input for the next transformation, and in each case a histogram is generated. The histograms are summed up bin-by-bin and the resulted vector used to index the image. The image blurring process uses pixel's neighbourhood information, making the proposed SCH exhibit the inherent textural information of the image that has been indexed. The SCH was extensively tested on the Coil100, Outext, Batik and Corel10K datasets. The Coil100, Outext, and Batik datasets are generally used to assess image texture descriptors, while Corel10K is used for heterogeneous descriptors. The experimental results show that our proposed descriptor significantly improves retrieval and classification rate when compared with (CMTH, MTH, TCM, CTM and NRFUCTM) which are the start-of-the-art descriptors for images with textural features. [ABSTRACT FROM AUTHOR]

The goal of exemplar-based texture synthesis is to generate texture images that are visually similar to a given exemplar. Recently, promising results have been reported by methods relying on convolutional neural networks (ConvNets) pretrained on large-scale image datasets. However, these methods have difficulties in synthesizing image textures with non-local structures and extending to dynamic or sound textures. In this article, we present a conditional generative ConvNet (cgCNN) model which combines deep statistics and the probabilistic framework of generative ConvNet (gCNN) model. Given a texture exemplar, cgCNN defines a conditional distribution using deep statistics of a ConvNet, and synthesizes new textures by sampling from the conditional distribution. In contrast to previous deep texture models, the proposed cgCNN does not rely on pre-trained ConvNets but learns the weights of ConvNets for each input exemplar instead. As a result, cgCNN can synthesize high quality dynamic, sound and image textures in a unified manner. We also explore the theoretical connections between our model and other texture models. Further investigations show that the cgCNN model can be easily generalized to texture expansion and inpainting. Extensive experiments demonstrate that our model can achieve better or at least comparable results than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) is a signal processing framework for effectively reconstructing signal from a small number of measurements obtained by linear projections of the signal. It is an ongoing challenge for the real-time image reconstruction of the computational imaging, including single pixel imaging based on CS. We built a block-based CS (BCS) image reconstruction framework via a deep learning network with smoothed projected Landweber (SPL). A fully connected network performs both BCS linear sensing and non-linear reconstruction stages, and SPL removes the blocking artifacts due to incorporate Wiener filtering into projected Landweber (PL) method at each iteration. The sensing matrix and nonlinear prediction operator are jointly optimized, and the smoothing filtering is coalesced into the PL framework for eliminating high-frequency oscillatory blocking artifact. Experimental results reveal that the optimized scheme outperforms the approach only based on deep neural network. The reconstruction quality can be improved while being only slightly slower, especially the gain of structural similarity is significantly better than peak signal-to-noise ratio, and the reconstruction image texture details are vivid and natural. At 10% sensing rate, the structural similarity maximum (minimum) gain reaches 0.098 (0.021). The proposed approach is not only far superior to other state-of-the-art CS algorithms in terms of reconstruction time and quality but also comparable with up-to-date deep learning methods. [ABSTRACT FROM AUTHOR]

• Surface roughness is a complex multiscale characteristic and multiple roughness indexes can be defined. • The simplified geostatistical-based algorithm presented describes key aspects of short-range roughness. • Roughness anisotropy is a key morphological characteristic. Surface roughness, i.e. local variations in elevation which indicate less and less local smoothness, is an important concept for the analysis of solid earth/planetary surfaces and of the related surface processes. Surface roughness is a general concept covering multiple aspects of the surface spatial variability structure, that can be characterized at different scales and with different metrics. Accordingly, it is not surprising the wide range of approaches and algorithms adopted for the analysis of surface roughness and the lack of an accepted standard for its evaluation. In this context, geostatistical-based roughness indexes are a valid solution, providing a good balance between flexibility of algorithms and interpretability of the results. However, despite the long record of applications and the well-known and robust theoretical framework, geostatistical-based surface roughness tools have still not gained momentum in the context of geomorphometric analysis. Many geomorphometric studies concerning with the evaluation of roughness indexes are based on popular approaches, such as the ones based on measures of dispersion of normal vectors to surface. Unfortunately, these indexes present some drawbacks in the capability to represent specific aspects of surface roughness and from the perspective of their interpretability. Accordingly, in this paper some of the key aspects and advantages of geostatistical-based approaches for roughness analysis are highlighted, including the relevance of roughness anisotropy. Then, a new approach tailored to the analysis of short-range roughness, requiring a minimum intervention by the user, is introduced discussing the advantages compared to the vector dispersion-based roughness. The new approach is implemented as open-source code both in R language using the functions of the "Terra" package as well as in Python for Esri ArcMap GIS. The methodological considerations and algorithms presented can be applied also in the broader context of image analysis. [ABSTRACT FROM AUTHOR]