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Nowadays relational position embedding is widely used in many large multi‐modal models. It begins with relational captioning (a branch of image captioning) and contains two procedures: geometric modelling and prior attention. However, there are some problems that remain unsolved in the conventional procedures. This paper reviews the shortcomings of geometric modelling and prior attention. Then, a new framework called relational guided transformer (RGT) is proposed to verify the authors' conclusion from the origin of relational position embedding—relational captioning. Specifically, RGT has two simple but effective improvements in geometric modelling and prior attention: (1) A machine‐learned geometric modelling strategy called multi‐task geometric modelling (MTG) is used under multi‐task learning, replacing the original hand‐made geometric feature. (2) The effectiveness of multiple kinds of prior attention is discussed and preserved in a better form, which is called spatial guided attention (SGA) to integrate the geometric prior knowledge. Extensive experiments on MSCOCO and Flickr30k have been performed to investigate the effectiveness of each module and prove our argument. The superiority of the model comparing to the authors' baseline has also been proven on the offline evaluation with the "Karpathy" test split of both datasets. [ABSTRACT FROM AUTHOR]

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

This paper investigates the effect of anisotropic resolution on the image textural features of pharmacokinetic (PK) parameters of a murine glioma model using dynamic contrast-enhanced (DCE) MR images acquired with an isotropic resolution at 7T with pre-contrast T1 mapping. The PK parameter maps of whole tumors at isotropic resolution were generated using the two-compartment exchange model combined with the three-site-two-exchange model. The textural features of these isotropic images were compared with those of simulated, thick-slice, anisotropic images to assess the influence of anisotropic voxel resolution on the textural features of tumors. The isotropic images and parameter maps captured distributions of high pixel intensity that were absent in the corresponding anisotropic images with thick slices. A significant difference was observed in 33% of the histogram and textural features extracted from anisotropic images and parameter maps, compared to those extracted from corresponding isotropic images. Anisotropic images in different orthogonal orientations demonstrated 42.1% of the histogram and textural features to be significantly different from those of isotropic images. This study demonstrates that the anisotropy of voxel resolution needs to be carefully considered when comparing the textual features of tumor PK parameters and contrast-enhanced images. [ABSTRACT FROM AUTHOR]

Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various requirements of different applications. In this paper, we first introduce the truncated Huber penalty function which shows strong flexibility under different parameter settings. A generalized framework is then proposed with the introduced truncated Huber penalty function. When combined with its strong flexibility, our framework is able to achieve diverse smoothing natures where contradictive smoothing behaviors can even be achieved. It can also yield the smoothing behavior that can seldom be achieved by previous methods, and superior performance is thus achieved in challenging cases. These together enable our framework capable of a range of applications and able to outperform the state-of-the-art approaches in several tasks, such as image detail enhancement, clip-art compression artifacts removal, guided depth map restoration, image texture removal, etc. In addition, an efficient numerical solution is provided and its convergence is theoretically guaranteed even the optimization framework is non-convex and non-smooth. A simple yet effective approach is further proposed to reduce the computational cost of our method while maintaining its performance. The effectiveness and superior performance of our approach are validated through comprehensive experiments in a range of applications. Our code is available at https://github.com/wliusjtu/Generalized-Smoothing-Framework . [ABSTRACT FROM AUTHOR]

An in-depth determination of date fruit properties belonging to a given variety can have an impact on their consumption, processing, and storage. The objective of this study was to characterize date fruits of the 'Mejhoul' variety using (i) objective and non-destructive image-analysis features and (ii) measurements of physicochemical parameters. Based on images acquired using a digital camera, more than 1600 texture parameters from the individual color channels L, a, b, R, G, B, X, Y, and Z, and 40 geometric characteristics (including linear dimensions and shape factors for each fruit), were determined. Additionally, pomological features, water content, water activity, color parameters (L*, a*, b*), total soluble solids (TSS), reducing sugars, and total sugars were measured. As a main result, the application of machine vision allowed for the correct detection of 'Mejhoul' dates and the determination of the image features. The differences in the values of the histogram's mean (HMean texture) for individual color channels were determined. The 'Mejhoul' date fruit images in color channel a (aHMean equal to 145.88) and color channel b (bHMean: 145.49) were the brightest, and in channel Z they were the darkest (ZHMean: 4.23). Due to the determination of the elliptic shape factor (W1) of 1.000 and the circular shape factor (W2) of 0.110, the elliptical shape of the fruit was confirmed. On the other hand, 'Mejhoul' dates were characterized by a length of 47.3 mm, a diameter of 26.4 mm, flesh thickness of 6.25 mm, total soluble solids of 62.1%, water content of 28.0%, water activity of 0.652, hardness of 694 g, reducing sugars of 13.8%, and total sugars of 58.8%. Due to the determination of many image features and other parameters, this paper presents the first comprehensive characterization of 'Mejhoul' date fruits using a non-destructive imaging technique linked to some physicochemical quality attributes. [ABSTRACT FROM AUTHOR]

Texture-based informal settlement (IS) mapping has gained attention in urban remote sensing (RS) research. Numerous studies conducted on the use of texture analysis for IS mapping have investigated wide-ranging sensors, algorithms, scale, classifiers, feature selection methods, and other factors of interest. However, no study has systematically investigated key factors affecting texture-based classification processes. This paper presents a detailed synthesis of scientific progress in texture based IS mapping. Results revealed that grey level co-occurrence matrix was the most popularly used algorithm.Quickbird was the widely used sensor. The use of machine-learning classifiers, particularly, support vector machine and random forest yielded, comparatively, high accuracies (>80%). Interestingly, deep learning showed potential to advance IS identification. Multi-city comparison studies demonstrated need for texture features to be locally specific in order to allow transferability. Thus, integration of RS data and field survey statistics could be crucial in enhancing understanding of morphological variations for improved IS mapping. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GANs) emerged thanks to the development of deep neural networks. Forgery images generated by various variants of GANs are widely spread on the Internet, which may be damage personal credibility and cause huge property losses. Thus, numerous methods are proposed to detect forgery images, but most of them are designed to detect forgery faces. Therefore, a method to detect forgery images of various scenes is proposed. In this work, central difference convolution and vanilla convolution (CDC‐Mix) are mixed after considering the depth and width features of neural networks and analyzing the influence of attention on network performance. Based on CDC‐Mix, a separable convolution (SeparableCDC‐Mix) is proposed. The proposed method consists of three parts: (1) CDC‐Mix and SeparableCDC‐Mix are used to extract the gradient information and texture features; (2) CDCM is used to extract the multi‐scale information of the image; (3) multi‐scale fusion module (MS‐Fusion) is used to fuse the multi‐scale information from different locations of the network. A large number of experiments have been carried out on several datasets generated by GAN, and the experimental results show that the proposed method has a great improvement compared with the existing advanced methods. [ABSTRACT FROM AUTHOR]

A novel three‐dimensional (3D) surface texture measure (3DSTM) is presented based on the micro‐geometry of paper surfaces to classify different paper substrates. This is useful to automatically determine whether a document is printed on the correct paper substrate to help identify fraud. We use a 4‐light source photometric stereo (PS) method to recover the dense 3D geometry of paper surfaces captured using a high‐resolution sensing device. We derive a unique 3DSTM for each paper type based on the shape index (SI) map generated from the surface normals of the 3D data. We show that the proposed 3DSTM can robustly and accurately classify paper substrates with different physical properties and different surface textures. The accuracy of the proposed method is validated over a dataset comprising of 21 printed and 22 non‐printed paper types and a measure of success over 92% is achieved. [ABSTRACT FROM AUTHOR]

This paper introduces a new HEp‐2 cell classification model under two phases. In the initial phase, the input image is segmented using the morphological operations (opening and closing), and the segmented image is given for Convolutional Neural Network (CNN) classifier that gives the classified output. In the second phase, the given input is processed under (i) segmentation process (ii) Feature Extraction, and (iii) Classification. From the segmented images; the features like Gray level co‐occurrence Matrix TGLCM) and Gray level Run Length Matrix (GLRM) are extracted. After extracting the features, they are subjected to a classification process, where Neural Network (NN) is used. Finally, the mean of both classified output (first phase and second phase) is considered to be the final classified output. As the main contribution, to enhance the classification accuracy, the hidden neurons of both classifiers (CNN and NN) are optimally chosen during the classification process. To make this possible, this paper aims to propose a new Randomized Update based Grey Wolf Optimization (RP‐GWO) algorithm. Finally, the performance of the implemented approach is compared over other conventional approaches and its superiority is proven with respect to certain measures. [ABSTRACT FROM AUTHOR]

This paper introduces a new HEp-2 cell classification model under two phases. In the initial phase, the input image is segmented using the morphological operations (opening and closing), and the segmented image is given for Convolutional Neural Network (CNN) classifier that gives the classified output. In the second phase, the given input is processed under (i) segmentation process (ii) Feature Extraction, and (iii) Classification. From the segmented images; the features like Gray level co-occurrence Matrix TGLCM) and Gray level Run Length Matrix (GLRM) are extracted. After extracting the features, they are subjected to a classification process, where Neural Network (NN) is used. Finally, the mean of both classified output (first phase and second phase) is considered to be the final classified output. As the main contribution, to enhance the classification accuracy, the hidden neurons of both classifiers (CNN and NN) are optimally chosen during the classification process. To make this possible, this paper aims to propose a new Randomized Update based Grey Wolf Optimization (RP-GWO) algorithm. Finally, the performance of the implemented approach is compared over other conventional approaches and its superiority is proven with respect to certain measures. [ABSTRACT FROM AUTHOR]

Face recognition systems are becoming ubiquitous, but they are vulnerable to spoofing attacks. The recently available light field cameras can be used for spoofing attack detection. In this study, the IST Lenslet Light Field Face Spoofing Database (IST LLFFSD) is proposed, consisting of 100 genuine images, from 50 subjects, captured with a Lytro ILLUM lenslet light field camera, and a set of 600 face spoofing attack images, captured using the same camera. The IST LLFFSD simulates six different types of presentation attacks, including printed paper, wrapped printed paper, laptop, tablet and two different mobile phones. This study also proposes a novel spoofing attack detection solution, based on a compact, yet effective, descriptor exploiting the colour and texture variations associated with the different directions of light captured in light field images. Extensive experiments show very effective results, with the proposed solution performing better than state‐of‐the‐art alternatives for the face spoofing attack types considered. [ABSTRACT FROM AUTHOR]

Cloud extraction and classification from satellite imagery is important for many applications in remote sensing. Satellite images are segmented based on distance, intensity and texture of the images. The popular segmentation algorithms, k‐means (KM) and fuzzy c‐means (FCM) clustering algorithms, face some problems such as unknown number of groups, unknown initialization and dead centers. In this paper, an unsupervised pixel classification by the KM and FCM algorithms is improved and the selection of centroids is made automatic. The proposed improved k‐means (IKM) and improved fuzzy c‐means (IFCM) clustering algorithms segment the INSAT‐3D satellite's thermal infrared image into low‐level, middle‐level, high‐level clouds and non‐cloudy region. As human beings can easily find the clouds in the satellite images, visible image is used to differentiate the clouds from the background. A threshold is found from the histogram of the visible image to separate the cloudy and non‐cloudy pixels. The other three thresholds to divide the clouds into three types are found from the thermal infrared image's histogram. The segmentation results of IKM and IFCM algorithms are compared with the existing segmentation algorithms. The comparison shows that IFCM algorithm matches well with original image followed by IKM algorithm as compared with existing algorithms. [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]

Many representation methods were built for gray image textures. However, they are not effective for color textures in general. To alleviate this problem, in this paper we propose a novel Compact Interchannel Sampling Difference Descriptor (CISDD) for color texture classification. In particular, considering sampling-based method can capture more directional information, we first use a heavy-tailed distribution, t-distribution to generate sample points in the image patch to calculate the micro-block difference. Then we model the interchannel relationship of color texture image by using dense micro-block differences. Furthermore, we utilize principal component analysis (PCA) to reduce the dimensions of the features encoded by the Fisher vector, and construct a Compact Interchannel Sampling Difference Descriptor (CISDD) for representing color texture image. Finally, experimental results on five published standard texture datasets (KTH-TIPS, VisTex, CUReT, USPTex and Colored Brodatz) reveal that CISDD is effective and outperforms thirteen representative color texture classification methods. [ABSTRACT FROM AUTHOR]

Computer vision applications usually rely on the features extracted from input images with good visibility. Image acquisition systems may produce degraded images with low contrast or distorted colours. For instance, bad weather (haze, fog) can cause images captured outdoor with low visibility. Image processing algorithms generally assume that the input image is the scene radiance. Haze removal, with the recovery of image radiance, ensures reliable features extracted from images and the image processing algorithm can achieve optimal performance. Inspired by the concept of image dehazing, the authors propose an image enhancement method that can be used to improve the visibility of the images. Each original image is first transformed into multiple exposure images by means of gamma‐correction operations and adaptive histogram equalization. The transformed images are analyzed by the computation of the local ternary pattern. The image is then enhanced, with each pixel generated from the set of transformed image pixels weighted by a function of the local pattern feature. The authors evaluate their proposed method on four benchmark image dehazing datasets. The quantitative results show that our method outperforms many deterministic algorithms and deep learning models. Moreover, the authors investigate the impact of image enhancement on a practical image‐based application—the reconstruction of three‐dimensional (3D) model of survey scene. Accurate 3D model reconstruction depends on high‐quality images. Degraded images will result in large errors in the reconstructed 3D model. Experimentations have been carried out on outdoor and indoor surveys. Our analysis finds that when fed into the photogrammetry software, the images enhanced by the authors' method can reconstruct 3D scene models with sub‐millimetre mean errors, which are much better than those with the original images. As shown in the visual and quantitative results of 3D model reconstruction, the authors' proposed method also outperforms other image enhancement methods. [ABSTRACT FROM AUTHOR]

Vulnerability to presentation attacks is the most valid issue of face-based authentication systems. Therefore, automatic detection of face spoofing plays a vital role in the safe use of face recognition applications in situations where the system works alone. In this work, we propose a method based on texture feature analysis. We select varying color channels among RGB, HSV, and YCbCr spaces depending on the minimum classification error rate to extract different wavelet sub-bands. Accordingly, the Green (G) channel of the RGB color spaces, the Saturation (S) of the HSV color space, the blue-difference Chroma (Cb) component of the YCbCr color space, and the Grayscale of the facial image to extract wavelet sub-band. The final texture feature vector was constructed using the Local Phase Quantization (LPQ) descriptor on the obtained wavelet sub-bands. Moreover, we use the genetic algorithm to reduce the feature vector's dimensions and minimize the classification error rate. The proposed method's performance was evaluated using inter-dataset and intra-dataset tests on nine public datasets. In these tests, the performance of the proposed method on 3Ddmad, HKBU-MARsV1+, Replay-Mobile, OULU, SiW, WMCA, CASIA-MFS, and MSU-MFSD datasets has proven to be better than the most advanced methods available. [ABSTRACT FROM AUTHOR]

The thermal noise-induced distortions in a Sentinel-1 terrain observation with progressive scans synthetic aperture radar image cannot be corrected by the noise equivalent sigma nought (NESZ) subtraction only. Since the thermal noise is scaled during synthetic aperture radar processing, it resides not only as an additive noise in each pixel but also as a multiplicative noise in the interpixel contrast. In this paper, we investigate the noise characteristics and propose an efficient method for the multiplicative textural noise correction. The core ideas are to find the optimal coefficient of the noise-induced standard deviation (SD) and model the noise contribution to the local SD as a function of the NESZ and the signal-to-noise ratio. Denoising is accomplished by a subwindow-wise adaptive rescaling of the pixel values. The improvements in the first- and second-order statistical textural features demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Several human being traits can be used as a robust and distinctive identifier for a given person. The palm region of the hand is one of these features that researchers in biometric fields have given a huge consideration in recent years. Many works have been proposed in the literature to design palmprint (an image acquired of the palm region) recognition framework. Extraction of prominent image local features is a critical module in most of these approaches. Local Binary Patterns (LBP) like methods, have emerged as one of the most effective feature extraction techniques. Despite a period of remarkable evolution, neither extensive and comprehensive evaluation nor comparison has been performed to date on a large number of LBP variants and non‐LBP texture methods in palmprint recognition problem. Motivated by this, this paper aims to fill that gap and provide a comprehensive comparative study of the performance of a large number of recent texture descriptors in palmprint recognition. Extensive experimental results on the well‐known constrained and unconstrained challenging palmprint databases, indicate that a number of tested local texture descriptors, which are evaluated for the first time on palmprint recognition, achieve promising results. Classification results are statistically compared through Wilcoxon signed rank test. [ABSTRACT FROM AUTHOR]

Degraded image semantic segmentation is of great importance in autonomous driving, highway navigation systems, and many other safety-related applications and it was not systematically studied before. In general, image degradations increase the difficulty of semantic segmentation, usually leading to decreased semantic segmentation accuracy. Therefore, performance on the underlying clean images can be treated as an upper bound of degraded image semantic segmentation. While the use of supervised deep learning has substantially improved the state of the art of semantic image segmentation, the gap between the feature distribution learned using the clean images and the feature distribution learned using the degraded images poses a major obstacle in improving the degraded image semantic segmentation performance. The conventional strategies for reducing the gap include: 1) Adding image-restoration based pre-processing modules; 2) Using both clean and the degraded images for training; 3) Fine-tuning the network pre-trained on the clean image. In this paper, we propose a novel Dense-Gram Network to more effectively reduce the gap than the conventional strategies and segment degraded images. Extensive experiments demonstrate that the proposed Dense-Gram Network yields state-of-the-art semantic segmentation performance on degraded images synthesized using PASCAL VOC 2012, SUNRGBD, CamVid, and CityScapes datasets. [ABSTRACT FROM AUTHOR]

During the process of watching 3D synthesised video (3D‐SV) and switching viewpoints, there is a case of asymmetric distortion, the left(right) viewpoint is a synthesised video generated by rendering technique, and the right(left) viewpoint is a real video taken by the camera. How to accurately estimate the quality of 3D‐SV with binocular asymmetric distortions is a new and challenging problem. Aiming at this problem, a blind quality assessment method for 3D‐SV with binocular asymmetric distortions is proposed. Firstly, the local edge deformations of synthesised videos at different scales are measured by calculating their standard deviations. Secondly, the global naturalness of synthesised videos is computed by analysing their natural statistical characteristics. Thirdly, a strategy for fusing left and right quality scores is proposed, which considers their texture information in different directions. Finally, the random forest is used to obtain an objective quality score. The experimental results show the superiority of the proposed method on asymmetry 3D‐SV database. [ABSTRACT FROM AUTHOR]

The objective of this paper is to assess the accuracy of C-band synthetic aperture radar (SAR) datasets in mapping peatland types over a region of Canada's subarctic boreal zone. This paper assessed contributions of quad-polarization linear backscatter intensities (σ°HH, σ°HV, σ°VV), image textures, and two polarimetric scattering decompositions: 1) Cloude–Pottier, and 2) Freeman–Durden. Four quad-polarimetric RADADSAT-2 images were studied at incidence angles of 19.4°, 23.1°, 45.8°, and 48.1°. The influence of combining dual-angular information acquired within a short temporal span was also assessed. These C-band SAR data were used to classify peatlands according to isolated flat bogs (bogs), channel fens (fens), raised peat plateaus (plateaus), and forested uplands (uplands) using a supervised support vector machine (SVM) classifier. Numerous classifications were examined to compare the unique contributions of these variables to classification accuracy. Results suggest linear backscatter variables in isolation produce comparable classification results with those of the Freeman–Durden and Cloude–Pottier decompositions. Combining polarimetric decomposition and texture data into classifications with linear backscatter data resulted in only minor (∼1–3%) improvement. Combining classifications from small and large incidence angles (dual-angular) significantly improved classification results over those of a single image. Classification accuracy was the highest for isolated bogs and open water surfaces, whereas fens, uplands, and plateaus had lower accuracies. The highest accuracy classification (84% and kappa coefficient of 0.80) used a dual-angular approach, with additional decomposition and texture information. However, it is noted that texture information rarely improved classification results across all tests. This approach identified isolated flat bogs, channel fens, and raised peat plateaus with >76% producer's accuracies. [ABSTRACT FROM PUBLISHER]

Fingerprint-based recognition systems have been increasingly deployed in various applications nowadays. However, the recognition systems can be spoofed by using an accurate imitation of a live fingerprint such as an artificially made fingerprint. In this paper, we propose a novel software-based fingerprint liveness detection method which achieves good detection accuracy. We regard the fingerprint liveness detection as a two-class classification problem and construct co-occurrence array from image gradients to extract features. In doing so, the quantization operation is firstly conducted on the images. Then, the horizontal and vertical gradients at each pixel are calculated, and the gradients of large absolute values are truncated into a reduced range. Finally, the second-order and the third-order co-occurrence arrays are constructed from the truncated gradients, and the elements of the co-occurrence arrays are directly used as features. The second-order and the third-order co-occurrence array features are separately utilized to train support vector machine classifiers on two publicly available databases used in Fingerprint Liveness Detection Competition 2009 and 2011. The experimental results have demonstrated that the features extracted with the third-order co-occurrence array achieve better detection accuracy than that with the second-order co-occurrence array and outperform the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Verity of information of the image in the form of image features is used in different application like pattern recognition, feature matching, image segmentation, image fusion, video processing, visual surveillance, medical diagnosis, traffic safety monitoring, remote sensing, human computer interaction, etc. Image is defined precisely and uniquely with the help of features which are useful in classifying and recognition of images. Extracting information from features of the image is a complex and diverse phenomenon. Retrieval of correct image information becomes very difficult for error reduction in the image processing. Lighting effect, zoom, distance, position, color model selected, angle of the object from camera etc. are considerable factors that affects the accuracy of feature detection from the image. In this paper we studied and experimentation using Viola Jones algorithm are performed on distance and zoom for object detection for mouth feature detection on primary face database which is captured by smartphone. Analysis of the result concludes that as distance between object and camera increases, false negatives (Type II error) increase in mouth feature detection and it goes increasingly if the camera goes far away from object. These false negatives can be reduced by increasing zoom of the camera to achieve the accuracy and improve the mouth feature detection. [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]

This study is concerned with analysing face–ocular multimodal biometric systems for a person gender prediction. Particularly, this is the first study considering fusion of face and ocular biometrics to predict gender of a person via a hybrid multimodal scheme. The authors aim to investigate the effect of multimodal biometric systems at score and feature‐level fusion on gender classification. The implementation of uniform local binary pattern (ULBP) feature extractor is taken into account to extract the face and ocular texture information. This paper proposes to select the efficient feature sets of both modalities using a novel evolutionary algorithm called backtracking search algorithm (BSA). On the other hand, support vector machine (SVM) is applied for classification purpose using the fused face and ocular features and scores. The proposed scheme is validated using CASIA‐Iris‐Distance and MBGC multimodal biometric databases with consideration of a subject‐disjoint training and testing evaluation. The achieved gender recognition demonstrates the superiority of the hybrid multimodal face–ocular scheme over unimodal face and ocular schemes implemented in this study for a subject gender prediction. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of writer identification from handwriting samples in Arabic. The proposed technique relies on extracting small fragments of writing which are characterised using two textural descriptors, Histogram of Oriented Gradients (HOG) and Gray Level Run Length (GLRL) Matrices. Similarity scores realised using HOG and GLRL features are combined using a number of fusion rules. The system is evaluated on three well‐known Arabic handwriting databases, the IFN/ENIT database with 411 writers, the KHATT database with 1000 writers, and QUWI database with 1,017 writers. Fusion using the 'sum' rule reports the highest identification rates reading 96.86, 85.40, and 76.27% on IFN/ENIT, KHATT, and QUWI databases, respectively. The results realised on the KHATT database are comparable to the state of the art while those reported on the IFN/ENIT and QUWI databases are the highest to the best of authors' knowledge. [ABSTRACT FROM AUTHOR]

Bilateral filters have been extensively utilized in a number of image denoising applications such as segmentation, registration, and tissue classification. However, it requires burdensome adjustments of the filter parameters to achieve the best performance for each individual image. To address this problem, this paper proposes a computer-aided parameter decision system based on image texture features associated with neural networks. In our approach, parallel computing with the GPU architecture is first developed to accelerate the computation of the conventional bilateral filter. Subsequently, a back propagation network (BPN) scheme using significant image texture features as the input is established to estimate the GPU-based bilateral filter parameters and its denoising process. The k-fold cross validation method is exploited to evaluate the performance of the proposed automatic restoration framework. A wide variety of T1-weighted brain MR images were employed to train and evaluate this parameter-free decision system with GPU-based bilateral filtering, which resulted in a speed-up factor of 208 comparing to the CPU-based computation. The proposed filter parameter prediction system achieved a mean absolute percentage error (MAPE) of 6% and was classified as "high accuracy". Our automatic denoising framework dramatically removed noise in numerous brain MR images and outperformed several state-of-the-art methods based on the peak signal-to-noise ratio (PSNR). The usage of image texture features associated with the BPN to estimate the GPU-based bilateral filter parameters and to automate the denoising process is feasible and validated. It is suggested that this automatic restoration system is advantageous to various brain MR image-processing applications. [ABSTRACT FROM AUTHOR]

This paper discusses using computer-aided diagnosis (CAD) to distinguish between hepatocellular carcinoma (HCC), i.e., the most common type of primary liver malignancy and a leading cause of death in people with cirrhosis worldwide, and liver abscess based on ultrasound image texture features and a support vector machine (SVM) classifier. Among 79 cases of liver diseases including 44 cases of liver cancer and 35 cases of liver abscess, this research extracts 96 features including 52 features of the gray-level co-occurrence matrix (GLCM) and 44 features of the gray-level run-length matrix (GLRLM) from the regions of interest (ROIs) in ultrasound images. Three feature selection models—(i) sequential forward selection (SFS), (ii) sequential backward selection (SBS), and (iii) F-score—are adopted to distinguish the two liver diseases. Finally, the developed system can classify liver cancer and liver abscess by SVM with an accuracy of 88.875%. The proposed methods for CAD can provide diagnostic assistance while distinguishing these two types of liver lesions. [ABSTRACT FROM AUTHOR]

Dynamic texture (DT) classification has attracted extensive attention in the field of image sequence analysis. The probability distribution model, which has been used to analysis DT, can describe well the distribution property of signals. Here, the authors introduce the finite mixtures of Gumbel distributions (MoGD) and the corresponding parameter estimation method based on expectation–maximisation algorithm. Then, the authors propose the DT features based on MoGD model for DT classification. Specifically, after decomposing DTs with the dual‐tree complex wavelet transform (DT‐CWT), the median values of complex wavelet coefficient magnitudes of non‐overlapping blocks in detail subbands are modelled with MoGDs. The model parameters are accumulated into a feature vector to describe DT. During the classification, a variational approximation version of the Kullback–Leibler divergence is used to measure the similarity between different DTs. The experimental evaluations on two popular benchmark DT data sets (UCLA and DynTex++) demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Pattern regeneration is one of the applications of reverse engineering technology in the textile field, which realises the process of textile‐pattern regeneration‐textile, and fundamentally provides an intelligent design means of textile. At present, the method of pattern regeneration in the jacquard fabric is to use the image segmentation algorithm to segment the image digitalised by unidirectional imaging, and then the segmented pattern could be identified to regeneration for the design of new fabrics. However, due to the concave and convex pattern textures on the surface of jacquard fabric, the traditional unidirectional imaging method cannot be used for the full characterisation of its structural information, resulting in unsatisfactory pattern segmentation effect. To solve this problem, a novel segmentation algorithm for jacquard patterns based on multi‐view image fusion was proposed in this study. Based on multi‐view image acquisition and fusion, the pattern image of jacquard fabric could be cluster‐segmented by extracting the complete texture information of the fused image and the actual colour information of the calibrated image. Compared with the traditional unidirectional imaging method, the experimental results show that the enhanced texture information of the fused image is more workable for the pattern segmentation, it validates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Local binary patterns (LBPs) are one of the attempts for gathering local features with face recognition algorithms. Although the application of LBP's in many recognition contents is too apparent, these methods have limited accuracy because of their threshold value. One problem is earning one value for two different regions with a diverse pixel neighbourhood, which causes mistakes in feature vector and decreases the discriminative power. In this study, the authors proposed a modified LBP that covers the LBP's disadvantages. The proposed approach is arithmetic coded LBP (ACLBP) that uses arithmetic coding process during LBP calculation instead of applying original thresholds. The proposed policy addresses the problem of returning one similar LBP value for two different patches. Moreover, the proposed method modifies LBP by using a different threshold for calculating the pixels differences. Using this algorithm, the authors conducted a genetic‐based feature fusion method by combining LBP and histogram of oriented gradients and ACLBP. The proposed approach could work better on LFW dataset, and the ORL dataset and Yale face dataset that shows the improving role of ACLBP in comparison with the earlier version of LBP. [ABSTRACT FROM AUTHOR]

Local binary patterns (LBPs) are one of the attempts for gathering local features with face recognition algorithms. Although the application of LBP's in many recognition contents is too apparent, these methods have limited accuracy because of their threshold value. One problem is earning one value for two different regions with a diverse pixel neighbourhood, which causes mistakes in feature vector and decreases the discriminative power. In this study, the authors proposed a modified LBP that covers the LBP's disadvantages. The proposed approach is arithmetic coded LBP (ACLBP) that uses arithmetic coding process during LBP calculation instead of applying original thresholds. The proposed policy addresses the problem of returning one similar LBP value for two different patches. Moreover, the proposed method modifies LBP by using a different threshold for calculating the pixels differences. Using this algorithm, the authors conducted a genetic-based feature fusion method by combining LBP and histogram of oriented gradients and ACLBP. The proposed approach could work better on LFW dataset, and the ORL dataset and Yale face dataset that shows the improving role of ACLBP in comparison with the earlier version of LBP. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a method to calculate the consistency of alpha masking to assess the robustness of the matting algorithm. This study evaluates consistent alpha masks based on the Gaussian–Hermite moment in combination with gradient amplitude and gradient direction. The gradient direction describes the appearance and shape of local objects in the image, and the gradient amplitude accurately reflects the contrast and texture changes of small details in the image. They selected Gaussian blur, pretzel noise, and combined noise to destroy the image, and then evaluated the consistency of the original alpha mask and noise alpha mask. To determine the robustness of the matting algorithm, they assessed the degree of consistency of the alpha mask using three different evaluation levels. The experimental results show that noise has a greater impact on the performance of the matting algorithm, which shows a decreasing trend as the noise level in the image deepens. In noisy images, the traditional matting algorithm exhibits better robustness compared to the recently proposed trap matting algorithm. Different matting algorithms present different adaptations to different noises. [ABSTRACT FROM AUTHOR]

Road monitoring plays a vital role in emergency disposal, traffic accident liability determination, traffic trend analyzes, and other fields. This paper designs a real-time virtual-real fusion framework for multiview surveillance of large-scale scene, in which 2D panorama, satellite texture, and 3D model are merged. The system is implemented with the following contributions. First, a novel automatic mosaic algorithm for multiview overlapped images is proposed. Feature points between adjacent cameras are detected via comotion statistics map. In addition, transformation model is obtained using random sample consensus algorithm. Second, multiview images are morphed to a same overhead viewpoint. Finally, 2D panoramic road image and 3D scene model are combined based on high-precision ground control points. The proposed framework has been successfully applied to a large road intersection. With the virtual-real fusion approach, observers can freely monitor and ramble in the large-scale 3-D road scene. [ABSTRACT FROM PUBLISHER]

Textures have become widely adopted as an essential tool for lesion detection and classification through analysis of the lesion heterogeneities. In this study, higher order derivative images are being employed to combat the challenge of the poor contrast across similar tissue types among certain imaging modalities. To make good use of the derivative information, a novel concept of vector texture is firstly introduced to construct and extract several types of polyp descriptors. Two widely used differential operators, i.e., the gradient operator and Hessian operator, are utilized to generate the first and second order derivative images. These derivative volumetric images are used to produce two angle-based and two vector-based (including both angle and magnitude) textures. Next, a vector-based co-occurrence matrix is proposed to extract texture features which are fed to a random forest classifier to perform polyp classifications. To evaluate the performance of our method, experiments are implemented over a private colorectal polyp dataset obtained from computed tomographic colonography. We compare our method with four existing state-of-the-art methods and find that our method can outperform those competing methods over 4%-13% evaluated by the area under the receiver operating characteristics curves. [ABSTRACT FROM AUTHOR]

Transferring a photo to a stylised image with beautiful texture has become one of the most popular topics in computer vision and the application of image processing. Controlling the stroke size of the texture is one of the challenging problems in this task. Recent representative methods for such problem introduce a pyramid model to regulate receptive fields in the network. Meanwhile, dilated convolutions are proved to be a very efficient way to adjust receptive fields without losing resolution. By combining the advantages of both approaches and making special optimisation for VGG19 model for style transfer tasks, the authors propose to exploit dilated convolutions to extract texture information endowing the network with stroke controllable. Several sets of contrast experiments were conducted and results show that their algorithm can generate more attractive stylisation images and control stroke size flexibly. It demonstrates the superiority of applying dilated convolutions as a texture extraction method for maintaining more texture information and controlling stroke size. [ABSTRACT FROM AUTHOR]

Infrared images typically contain obvious strip noise. It is a challenging task to eliminate such noise without blurring fine image details in low-textured infrared images. In this paper, we introduce an effective single-image-based algorithm to accurately remove strip-type noise present in infrared images without causing blurring effects. First, a 1-D row guided filter is applied to perform edge-preserving image smoothing in the horizontal direction. The extracted high-frequency image part contains both strip noise and a significant amount of image details. Through a thermal calibration experiment, we discover that a local linear relationship exists between infrared data and strip noise of pixels within a column. Based on the derived strip noise behavioral model, strip noise components are accurately decomposed from the extracted high-frequency signals by applying a 1-D column guided filter. Finally, the estimated noise terms are subtracted from the raw infrared images to remove strips without blurring image details. The performance of the proposed technique is thoroughly investigated and is compared with the state-of-the-art 1-D and 2-D denoising algorithms using captured infrared images. [ABSTRACT FROM AUTHOR]

In this paper, we present the results of applying global morphological texture descriptors to the problem of content-based remote sensing image retrieval. Specifically, we explore the potential of recently developed multiscale texture descriptors, namely, the circular covariance histogram and the rotation-invariant point triplets. Moreover, we introduce a couple of new descriptors, exploiting the Fourier power spectrum of the quasi-flat-zone-based scale space of their input. The descriptors are evaluated with the UC Merced Land Use-Land Cover data set, which has been only recently made public. The proposed approach is shown to outperform the best known retrieval scores, despite its shorter feature vector length, thus asserting the practical interest of global content descriptors as well as of mathematical morphology in this context. [ABSTRACT FROM AUTHOR]

Wavelet‐based segmentation approaches are widely used for texture segmentation purposes because of their ability to characterise different textures. In this study, the authors assess the influence of the chosen wavelet and propose to use the recently introduced empirical wavelets. We show that the adaptability of the empirical wavelet permits to reach better results than classic wavelets. To focus only on the textural information, they also propose to perform a cartoon + texture decomposition step before applying the segmentation algorithm. The proposed method is tested on six classic benchmarks, based on several popular texture images. [ABSTRACT FROM AUTHOR]

This study investigates the generation of spatially distributed roughness coefficient maps based on image analysis and the extent to which those roughness coefficient values affect the flood inundation modeling using different hydraulic/hydrodynamic modeling approaches ungauged streams. Unmanned Aerial Vehicle (UAV) images were used for the generation of high-resolution Orthophoto mosaic (1.34 cm/px) and Digital Elevation Model (DEM). Among various pixel-based and object-based image analyses (OBIA), a Grey-Level Co-occurrence Matrix (GLCM) was eventually selected to examine several texture parameters. The combination of local entropy values (OBIA method) with Maximum Likelihood Classifier (MLC; pixel-based analysis) was highlighted as a satisfactory approach (65% accuracy) to determine dominant grain classes along a stream with inhomogeneous bed composition. Spatially distributed roughness coefficient maps were generated based on the riverbed image analysis (grain size classification), the size-frequency distributions of river bed materials derived from field works (grid sampling), detailed land use data, and the usage of several empirical formulas that used for the estimation of Manning's n values. One-dimensional (1D), two-dimensional (2D), and coupled (1D/2D) hydraulic modeling approaches were used for flood inundation modeling using specific Manning's n roughness coefficient map scenarios. The validation of the simulated flooded area was accomplished using historical flood extent data, the Critical Success Index (CSI), and CSI penalization. The methodology was applied and demonstrated at the ungauged Xerias stream reach, Greece, and indicated that it might be applied to other Mediterranean streams with similar characteristics and flow conditions. [ABSTRACT FROM AUTHOR]

The actual image resolution achieved by synthetic aperture systems often differs from the theoretical performance, and it is advantageous to automatically measure the realized resolution of these image products to monitor their quality. This article describes a novel image processing algorithm for measuring resolution based on the detection of prominent point scatterers. The algorithm proceeds in the following three main steps. 1) Identify candidate point scatterers via connected-component labeling and dual thresholding. 2) Prune the candidate scatterers and measure the size of the accepted detections by fitting an ellipse to the half-power contour traced in an oversampled image snippet. 3) Calculate the mode of the ellipse semiaxes lengths from a collection of scatterers to represent the local resolution estimate. The fitting of an ellipse to the half-power contour is a new contribution to resolution estimation that facilitates both detection and measurement, and it provides estimates of resolution in any direction, instead of only the range and along-track directions, which is important for images with excessive sensor motion. The accuracy and robustness of the algorithm are tested by applying it to simulated imagery with a series of parameter sweeps. Finally, the algorithm is applied to collected synthetic aperture sonar data and shown to be insensitive to seafloor textures. [ABSTRACT FROM AUTHOR]

Eddy current pulsed thermography (ECPT) is implemented for detection and separation of impact damage and resulting damages in carbon fiber reinforced plastic (CFRP) samples. Complexity and nonhomogeneity of fiber texture as well as multiple defects limit detection identification and characterization from transient images of the ECPT. In this paper, an integration of principal component analysis (PCA) and independent component analysis (ICA) on transient thermal videos has been proposed. This method enables spatial and temporal patterns to be extracted according to the transient response behavior without any training knowledge. In the first step, using the PCA, the data is transformed to orthogonal principal component subspace and the dimension is reduced. Multichannel morphological component analysis, as an ICA method, is then implemented to deal with the sparse and independence property for detecting and separating the influences of different layers, defects, and their combination information in the CFRP. Because different transient behaviors exist, multiple types of defects can be identified and separated by calculating the cross-correlation of the estimated mixing vectors between impact the ECPT sequences and nondefect ECPT sequences. [ABSTRACT FROM PUBLISHER]

To overcome the staircase effects and simultaneously avoid edge blurring, here the authors proposed a novel algorithm of adaptive fourth-order partial differential equation (PDE) for image smoothing. The proposed method is based on You and Kaveh (YK) model that first performs a pre-processing using coherence-enhancing diffusion filter before the smoothing. Then, the authors select an adaptive edge-stopping function founded on the Tansig function to effectively remove the noise and insignificant details without destruction of important edges and textures in the image. MATLAB simulation results validate the efficiency of the proposed model quantitatively and visually, when compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The constant false alarm rate (CFAR) detection method based on multi-look polarisation whitening filter (MPWF) has not been obtained in the polarimetric synthetic aperture radar imagery when the texture variable obeys the Inverse Beta distribution. To solve the problem, a new CFAR detection method with analytical expression for false alarm rate is proposed. First, the probability density function (PDF) of the output via MPWF is derived based on the hypothesis of the texture variables obeying the Inverse Beta distribution in the product model. Secondly, the analytical expression of false alarm rate about detection threshold is obtained by integrating the PDF of MPWF output, and the procedure of the CFAR detection method is designed. Finally, the performance comparisons are made among the new method, Wishart CFAR detection algorithm and the two-parameter CFAR method via simulated and real data of AIRSAR. The results show that figure of merit of the new method is the highest and the new method gives the best performance in Inverse Beta distribution case. [ABSTRACT FROM AUTHOR]

To achieve an automatic and accurate segmentation of lungs and improve the clinical efficiency of computer‐aided diagnosis, the authors present a lung segmentation algorithm based on the random forest method and a multi‐scale edge detection technique. The algorithm carries a first step of lung region extraction and a second step of lung nodule segmentation. By combining texture information, the improved superpixel generation method can better deal with initial segmentation on lung computed tomography images with inhomogeneous intensity. Then, the lung region is further extracted by using the random forest classifier on the superpixel features, and the lung contours are corrected with a proposed circle tracing technique. Finally, the segmentation is further refined by employing a multi‐scale edge detection technique, which enables their method to detect suspicious nodules with various intensities and sizes adaptively. The effectiveness of the proposed approach is demonstrated on a group of datasets by comparing with the corresponding ground truths as well as the classical algorithms. Experimental results show that the proposed method has a higher precision than the compared algorithms in a fully automatic fashion. [ABSTRACT FROM AUTHOR]

With the development of image processing technology and the improvement of unmanned aerial vehicle (UAV) manufacture, it has been becoming a hot research topic to inspect the transmission line's failure. Here, a method of locating the area of insulator strings in the aerial image captured by UAV system is been introduced. At first, the aerial image is converted to HSV colour space model, and the components of H and S are filtered to segment the insulator strings and background areas. Considering some background areas have the similar colour with the insulator string, the result of segmentation will contain some background areas. Then, the image of result is divided into small pieces with non-overlapping windows. The information of texture on each window is used to remove the remaining background area. The outline of the insulator strings in the image can be obtained. It is easily to detect the edge of the insulator string and locate the area of the insulator string using the outline image. At last, some insulator string images have been used to illustrate the processing of the method. Compared with the method using RGB colour space model, the results of method here is effective and reliable. [ABSTRACT FROM AUTHOR]

Finger texture (FT) images acquired from different spectral lighting sensors reveal various features. This inspires the idea of establishing a recognition model between FT features collected using two different spectral lighting forms to provide high recognition performance. This can be implemented by establishing an efficient feature extraction and effective classifier, which can be applied to different FT patterns. So, an effective feature extraction method called the surrounded patterns code (SPC) is adopted. This method can collect the surrounded patterns around the main FT features. It is believed that these patterns are robust and valuable. Furthermore, a novel classifier termed the re‐enforced probabilistic neural network (RPNN) is proposed. It enhances the capability of the standard PNN and provides better recognition performance. Two types of FT images from the multi‐spectral Chinese Academy of Sciences Institute of Automation (CASIA) database were employed as two types of spectral sensors were used in the acquiring device: the white (WHT) light and spectral 460 nm of blue (BLU) light. Supporting comparisons were performed, analysed and discussed. The best results were recorded for the SPC by enhancing the equal error rates at 4% for spectral BLU and 2% for spectral WHT. These percentages have been reduced to 0% after utilising the RPNN.[AQ ID=Q5] [ABSTRACT FROM AUTHOR]

This paper discusses the problem of reduced-resolution transcoding of compressed video bitstreams. An analysis of drift errors is provided to identify the sources of quality degradation when transcoding to a lower spatial resolution. Two types of drift error are considered: a reference picture error, which has been identified in previous works, and error due to the noncommutative property of motion compensation and down-sampling, which is unique to this work. To overcome these sources of error, four novel architectures are presented. One architecture attempts to compensate for the reference picture error in the reduced resolution, while another architecture attempts to do the same in the original resolution. We present a third architecture that attempts to eliminate the second type of drift error and a final architecture that relies on an intrablock refresh method to compensate for all types of errors. In all of these architectures, a variety of macroblock level conversions are required, such as motion vector mapping and texture down-sampling. These conversions are discussed in detail. Another important issue for the transcoder is rate control. This is especially important for the intra-refresh architecture since it must find a balance between number of intrablocks used to compensate for errors and the associated rate-distortion characteristics of the low-resolution signal. The complexity and quality of the architectures are compared. Based on the results, we find that the intra-refresh architecture offers the best tradeoff between quality and complexity and is also the most flexible. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a micro-macro feature combination approach for texture classification. The two disparate yet complementary categories of features are combined. By this way, Local Binary Pattern (LBP) plays the role of micro-structure feature extractor while the scattering transform captures macro-structure information. In fact, for extracting the macro-type features, coefficients are aggregated from three different layers of the scattering network. It is a handcrafted convolution network which is implemented by computing consecutively wavelet transforms and modulus non-linear operators. By contrast, in order to extract micro-structure features which are rotation-invariant, relatively robust to noise and illumination change, the completed LBP is utilized alongside the biologically-inspired filtering (BF) preprocessing technique. Overall, since the proposed framework can exploit the advantages of both feature types, its texture representation is not only invariant to rotation, scaling, illumination change but also highly discriminative. Intensive experiments conducted on many texture benchmarks such as CUReT, UIUC, KTH-TIPS-2b, and OUTEX show that our framework has a competitive classification accuracy. [ABSTRACT FROM AUTHOR]

After seminal papers over the period 2009 - 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach. [ABSTRACT FROM AUTHOR]