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The microtexture of the asphalt pavement, which is critical for the skid resistance, is dictated by the microtexture of the coarse aggregates used in the surface courses. The existing standard test methods can only capture the combined measure of coarse aggregate surface texture which can be influenced by other aggregate characteristics such as shape and angularity. Consequently, the surface texture of aggregates cannot be quantified explicitly using these tests, and therefore image analysis methods that can extract textural features are gaining more significance. In the present study, various image texture analysis methods are evaluated to identify a reliable texture indicator. Methods include traditional statistical approaches such as histogram method, Grey Level Co-occurrence Matrix (GLCM) methods, and transform-based wavelet texture analysis. The image analysis was carried out on the grey-scale scanning electron microscope (SEM) images before and after accelerated polishing. To compare the image texture parameters, the average surface roughness (Sa) measured from aggregate surface profile data obtained from a 3D Optical Surface Profilometer (3D OSP) was considered as a direct measure of surface texture. A detailed correlation analysis with Sa indicates that the surface texture index (STI) from the wavelet analysis quantifies the microtexture more accurately compared to statistical methods. [ABSTRACT FROM AUTHOR]

With the rapid growth in the number of motor vehicles worldwide, the general public is beginning to attach importance to the quality inspection of wheels before they leave the factory. The current wheel defect detection systems are often cumbersome to operate and have low practical performance. Therefore, this research will use dynamic image segmentation, image texture feature extraction and Back Propagation neural network classification based on wheel image defect feature analysis algorithm to achieve automatic intelligent detection of automotive wheel defects. In this study, an intelligent detection system for automotive wheel defects is also designed, and finally the performance of the detection system is tested experimentally to illustrate its practicality. The experimental results show that the proposed intelligent detection system for automotive wheel defects based on image texture features identifies defects in wheel castings with a correct rate of 96% and a false positive rate of only 2%. This illustrates that the detection system proposed in this study has a high recognition rate and can provide a useful reference for the automotive industry inspection. [ABSTRACT FROM AUTHOR]

The purpose of image smoothing is to improve the visual effect of the image and improve the clarity of the image, so as to make the image more conducive to computer processing and various feature analysis. Because the current technology fails to smooth the preprocessed image, it leads to the extraction of image texture features. The anti-interference performance is weak. For this reason, an image texture feature extraction technology based on the digital twin is proposed. Similarity analysis is carried out through the internal structure of the image, and the image is smoothed by the semisupervised learning method. On the basis of optimizing the denoised image through digital twinning, detect target feature points in the original image, then remove the abnormal and split feature points, assign the direction of image texture feature points, and build a fuzzy back propagation neural network model. Image texture feature extraction technology is implemented. The experimental results show that, compared with the traditional method, the proposed technique has a strong identification of original image features, and has a strong consistency with original data, and has a strong ability to resist the influence of abnormal data, noise, or redundant feature points. [ABSTRACT FROM AUTHOR]

Carbon fiber plain-woven prepreg is one of the basic materials in the field of composite material design and manufacturing, in which defect identification is an important and easily neglected part of testing. Here, a novel high recognition rate inspection method for carbon fiber plain-woven prepregs is proposed for inspecting bubble and wrinkle defects based on image texture feature compression. The proposed method attempts to divide the image into non-overlapping block lattices as texture primitives and compress them into a binary feature matrix. Texture features are extracted using a gray level co-occurrence matrix. The defect types are further defined according to texture features by k-means clustering. The performance is evaluated in some existing computer vision and machine learning methods based on fiber recognition. By comparing the result, an overall recognition rate of 0.944 is achieved, which is competitive with the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Image processing has played a relevant role in various industries, where the main challenge is to extract specific features from images. Specifically, texture characterizes the phenomenon of the occurrence of a pattern along the spatial distribution, taking into account the intensities of the pixels for which it has been applied in classification and segmentation tasks. Therefore, several feature extraction methods have been proposed in recent decades, but few of them rely on entropy, which is a measure of uncertainty. Moreover, entropy algorithms have been little explored in bidimensional data. Nevertheless, there is a growing interest in developing algorithms to solve current limits, since Shannon Entropy does not consider spatial information, and SampEn2D generates unreliable values in small sizes. We introduce a proposed algorithm, EspEn (Espinosa Entropy), to measure the irregularity present in two-dimensional data, where the calculation requires setting the parameters as follows: m (length of square window), r (tolerance threshold), and ρ (percentage of similarity). Three experiments were performed; the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters m, r, and ρ. Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data; the recommended parameters for better performance are m = 3, r = 20, and ρ = 0.7. [ABSTRACT FROM AUTHOR]

As one of fundamental texture classification methods, LBP-based descriptors have attracted considerable attention due to the efficiency, simplicity, and high performance. However, most of binary pattern methods cannot effectively capture the texture information with scale changes. Inspired by this, this paper proposes a multi-scale threshold integration encoding strategy for texture classification. The essence of this strategy is to introduce the multi-scale local texture information in the view of thresholding. Based on this, we propose the local multi-scale center pattern, local multi-scale sign pattern, and local multi-scale magnitude pattern to extract and describe the multi-scale local texture information. Then, the three sub-patterns are jointly combined to generate the final descriptor for texture classification tasks. The experimental results on three popular texture databases significantly demonstrate that the proposed texture descriptor is very discriminative and powerful for visual texture classification tasks. [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]

Finding patches is a crucial step in a pavement performance survey. The study develops a machine learning and image processing algorithm-established automatic method for identifying asphalt pavement patches. The GrayLevel Co-Occurrence Matrix and image texture-based features derived from color channel statistics are used as input parameters to describe the condition of the pavement. For feature extraction, image processing methods like the projective integral of images, steerable filters, and an improved image thresholding method were used. Support Vector Machine is employed for categorizing the differentiating patched regions from non-patch ones. The suggested combination of image texture analytical methods has been trained using an IA data set created from 400 image samples. The feature set, which combines the characteristics of cracked objects with those generated from the projective integral, can produce the desired result. To make the model's execution simpler, a patch recognition program was created and implemented in MATLAB. As a result, the recently created method has the potential to be an instrument for traffic management organizations through the assessment of pavement performance. The results of the experiments indicate that the newly developed method may achieve excellent accuracy prediction results with a classifier performance rate of about 96%. Such a method could enable transportation authorities to assess the state of asphalt pavement. [ABSTRACT FROM AUTHOR]

Cardiac magnetic resonance imaging (MRI) usually requires a long acquisition time. The movement of the patients during MRI acquisition will produce image artifacts. Previous studies have shown that clear MR image texture edges are of great significance for pathological diagnosis. In this paper, a motion artifact reduction method for cardiac MRI based on edge enhancement network is proposed. Firstly, the four-plane normal vector adaptive fractional differential mask is applied to extract the edge features of blurred images. The four-plane normal vector method can reduce the noise information in the edge feature maps. The adaptive fractional order is selected according to the normal mean gradient and the local Gaussian curvature entropy of the images. Secondly, the extracted edge feature maps and blurred images are input into the de-artifact network. In this network, the edge fusion feature extraction network and the edge fusion transformer network are specially designed. The former combines the edge feature maps with the fuzzy feature maps to extract the edge feature information. The latter combines the edge attention network and the fuzzy attention network, which can focus on the blurred image edges. Finally, extensive experiments show that the proposed method can obtain higher peak signal-to-noise ratio and structural similarity index measure compared to state-of-art methods. The de-artifact images have clear texture edges. [ABSTRACT FROM AUTHOR]

Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions. [ABSTRACT FROM AUTHOR]

This study aimed to identify a distant-recurrence image biomarker in NSCLC by investigating correlations between heterogeneity functional gene expression and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) image features of NSCLC patients. RNA-sequencing data and 18F-FDG PET images of 53 patients with NSCLC (19 with distant recurrence and 34 without recurrence) from The Cancer Imaging Archive and The Cancer Genome Atlas Program databases were used in a combined analysis. Weighted correlation network analysis was performed to identify gene groups related to distant recurrence. Genes were selected for functions related to distant recurrence. In total, 47 image features were extracted from PET images as radiomics. The relationship between gene expression and image features was estimated using a hypergeometric distribution test with the Pearson correlation method. The distant recurrence prediction model was validated by a random forest (RF) algorithm using image texture features and related gene expression. In total, 37 gene modules were identified by gene-expression pattern with weighted gene co-expression network analysis. The gene modules with the highest significance were selected (p-value < 0.05). Nine genes with high protein–protein interaction and area under the curve (AUC) were identified as hub genes involved in the proliferation function, which plays an important role in distant recurrence of cancer. Four image features (GLRLM_SRHGE, GLRLM_HGRE, SUVmean, and GLZLM_GLNU) and six genes were identified to be correlated (p-value < 0.1). AUCs (accuracy: 0.59, AUC: 0.729) from the 47 image texture features and AUCs (accuracy: 0.767, AUC: 0.808) from hub genes were calculated using the RF algorithm. AUCs (accuracy: 0.783, AUC: 0.912) from the four image texture features and six correlated genes and AUCs (accuracy: 0.738, AUC: 0.779) from only the four image texture features were calculated using the RF algorithm. The four image texture features validated by heterogeneity group gene expression were found to be related to cancer heterogeneity. The identification of these image texture features demonstrated that advanced prediction of NSCLC distant recurrence is possible using the image biomarker. [ABSTRACT FROM AUTHOR]

Objective. While simulated low-dose CT images and phantom studies cannot fully approximate subjective and objective effects of deep learning (DL) denoising on image quality, live animal models may afford this assessment. This study is to investigate the potential of DL in CT dose reduction on image quality compared to iterative reconstruction (IR). Approach. The upper abdomen of a live 4 year old sheep was scanned on a CT scanner at different exposure levels. Images were reconstructed using FBP and ADMIRE with 5 strengths. A modularized DL network with 5 modules was used for image reconstruction via progressive denoising. Radiomic features were extracted from a region over the liver. Concordance correlation coefficient (CCC) was applied to quantify agreement between any two sets of radiomic features. Coefficient of variation was calculated to measure variation in a radiomic feature series. Structural similarity index (SSIM) was used to measure the similarity between any two images. Diagnostic quality, low-contrast detectability, and image texture were qualitatively evaluated by two radiologists. Pearson correlation coefficient was computed across all dose-reconstruction/denoising combinations. Results. A total of 66 image sets, with 405 radiomic features extracted from each, are analyzed. IR and DL can improve diagnostic quality and low-contrast detectability and similarly modulate image texture features. In terms of SSIM, DL has higher potential in preserving image structure. There is strong correlation between SSIM and radiologists’ evaluations for diagnostic quality (0.559) and low-contrast detectability (0.635) but moderate correlation for texture (0.313). There is moderate correlation between CCC of radiomic features and radiologists’ evaluation for diagnostic quality (0.397), low-contrast detectability (0.417), and texture (0.326), implying that improvement of image features may not relate to improvement of diagnostic quality. Conclusion. DL shows potential to further reduce radiation dose while preserving structural similarity, while IR is favored by radiologists and more predictably alters radiomic features. [ABSTRACT FROM AUTHOR]

Image texture is an important part of many types of images, for example medical images. Texture Analysis is the technique that uses measurable features to categorize complex textures. The main goal is to extract discriminative features that are used in different pattern recognition applications and texture categorization. This paper investigates the extraction of most discriminative features for different texture images from the “Colored Brodatz” dataset using two types of image contrast measures, as well as using the statistical moments on five bands (red, green, blue, grey, and black). The Euclidean distance measure is used in the matching step to check the similarity degree. The proposed method was tested on 112 classes of textures. The achieved results showed that the proposed method is accurate and fast concerning classification accuracy with low computational complexity. The achieved Recognition Rate (RR) was 100%. [ABSTRACT FROM AUTHOR]

• An almost complete review of current methods for feature extraction from underwater acoustic signals. • Developing a reliable and accurate model for distinguishing Underwater vessel-radiated acoustical noise (UVRAN), which is robustness to noise. • Converting complex marine signals to images using the Gramian angular field (GAF) technique and extracting second-order image statistics by image texture analysis for the first time. • extracting more discriminative information from the UVRAN by signal-to-image transformation. • Designing a parameter called spectral amplitude mean difference function (SAMDF) that is more suitable for encoding to an image than the original signal. Underwater vessel-radiated acoustical noise (UVRAN) is a major factor for classification in the sea by the SONAR. Due to unsteady and complex maritime ambient, analyzing underwater sound signals is a challenging issue that has lately received attention in the marine field. In the conventional feature extraction methods, to reduce the effect of ocean noise, the de-noising procedure is performed before complexity measurement by mode decomposition techniques. Based on this, we propose a novel insight for the first time to distinguish the objects which made the underwater noises as the hydro-acoustic signature, using a signals-to-image conversion without noise removal. After pre-processing, the spectral amplitude mean difference function is encoded into an image using Gramian angular field (GAF) technique. Subsequently, image texture analysis is performed in which GAF images are subjected to the gray-level co-occurrence matrix (GLCM). Finally, the second-order image statistic (i.e., 2-D permutation entropy) is calculated. Compared with other methods, results demonstrate that the proposed method has a high degree of separation and stability between the various kinds of underwater targets, suggesting that the methodology is superior to the existing methods. Moreover, our model is robust to noise. The approach perhaps opens an alternative path for UVRAN discrimination. [ABSTRACT FROM AUTHOR]

In the field of single-image super-resolution reconstruction, GAN can obtain the image texture more in line with the human eye. However, during the reconstruction process, it is easy to generate artifacts, false textures, and large deviations in details between the reconstructed image and the Ground Truth. In order to further improve the visual quality, we study the feature correlation between adjacent layers and propose a differential value dense residual network to solve this problem. We first use the deconvolution layer to enlarge the features, then extract the features through the convolution layer, and finally make a difference between the features before being magnified and the features after being extracted so that the difference can better reflect the areas that need attention. In the process of extracting the differential value, using the dense residual connection method for each layer can make the magnified features more complete, so the differential value obtained is more accurate. Next, the joint loss function is introduced to fuse high-frequency information and low-frequency information, which improves the visual effect of the reconstructed image to a certain extent. The experimental results on Set5, Set14, BSD100, and Urban datasets show that our proposed DVDR-SRGAN model is improved in terms of PSNR, SSIM, and LPIPS compared with the Bicubic, SRGAN, ESRGAN, Beby-GAN, and SPSR models. [ABSTRACT FROM AUTHOR]

Background: Deep learning surpasses many traditional methods for many vision tasks, allowing the transformation of hierarchical features into more abstract, high-level features. Purpose: To evaluate the prognostic value of preoperative computed tomography (CT) image texture features and deep learning self-learning high-throughput features (SHF) on postoperative overall survival in the treatment of patients with colorectal cancer (CRC). Material and Methods: The dataset consisted of 810 enrolled patients with CRC confirmed from 10 November 2011 to 10 February 2018. In contrast, SHF extracted by deep learning with multi-task training mechanism and texture features were extracted from the CT with tumor volume region of interest, respectively, and combined with the Cox proportional hazard (CoxPH) model for initial validation to obtain a RAD score to classify patients into high- and low-risk groups. The SHF stability was further validated in combination with Neural Multi-Task Logistic Regression (N-MTLR) model. The overall recognition ability and accuracy of CoxPH and N-MTLR model were evaluated by C-index and Integrated Brier Score (IBS). Results: SHF had a more significant degree of differentiation than texture features. The result is (SHF vs. texture features: C-index: 0.884 vs. 0.611; IBS: 0.025 vs. 0.073) in the CoxPH model, and (SHF vs. texture features: C-index: 0.861 vs. 0.630; IBS: 0.024 vs. 0.065) in N-MTLR. Conclusion: SHF is superior to texture features and has potential application for the preoperative prediction of the individualized treatment of CRC. [ABSTRACT FROM AUTHOR]

In the last few decades, many studies have been performed with the main hope of utilizing imaging methods so as to detect static (bubble size and shape, color, texture) and dynamic (velocity and stability) features of froth that involve crucial information about the process state in order to assess and monitor the performance of flotation process. Although several types of flotation automated control system problems are being successfully solved using the various techniques of features extraction from froth images, there are still a number of unresolved subjects and obstacles. Hence, a suitable review of these methods is needed. After reviewing the technical aspects of froth images structural features in part 1 of this two-part review paper, this work provides an overview of several different approaches for the sake of analyzing and classifying froth image texture and dynamic features. Finally, we conclude our review by linking concentrate grade and recovery with the froth image features described in this two-part paper. In the close future, it is expected with ever-growing application needs and research progress, image analysis systems are becoming even more effective solutions for monitoring and controlling flotation performance. This review provides a platform for future initiatives and potential developments in this area. [ABSTRACT FROM AUTHOR]

Image deblurring attracts research attention in the field of image processing and computer vision. Traditional deblurring methods based on statistical prior largely depend on the selected prior type, which limits their restoring ability. Moreover, the constructed deblurring model is difficult to solve, and the operation is comparatively complicated. Meanwhile, deep learning has become a hotspot in various fields in recent years. End-to-end convolutional neural networks (CNNs) can learn the pixel mapping relationships between degraded images and clear images. In addition, they can also obtain the result of effectively eliminating spatial variable blurring. However, conventional CNNs have some disadvantages in generalization ability and details of the restored image. Therefore, this paper presents an iterative dual CNN called IDC for image deblurring, where the task of image deblurring is divided into two sub-networks: deblurring and detail restoration. The deblurring sub-network adopts a U-Net structure to learn the semantical and structural features of the image, and the detail restoration sub-network utilizes a shallow and wide structure without downsampling, where only the image texture features are extracted. Finally, to obtain the deblurred image, this paper presents a multiscale iterative strategy that effectively improves the robustness and precision of the model. The experimental results showed that the proposed method has an excellent effect of deblurring on a real blurred image dataset and is suitable for various real application scenes. [ABSTRACT FROM AUTHOR]

In order to realize the evaluation of regional comprehensive disaster reduction capacity in a complex environment, an evaluation model of regional comprehensive disaster reduction capacity in a complex environment based on remote sensing monitoring and data image feature analysis is proposed. According to the geographical location and scale of disaster spots and the parameter analysis of the model of disaster-bearing bodies around the disaster spots, the remote sensing monitoring method is adopted to extract the geographical remote sensing images of regional disaster spots in a complex environment. The collected geographical remote sensing images of regional disaster points under the complex environmental background are filtered and preprocessed, and the texture parameters of the geographical remote sensing images of regional disaster points under the complex environmental background are recognized by combining the method of image texture feature extraction. Based on the method of tone mapping, the rapid filtering and feature analysis of the geographical remote sensing images of regional disaster points under the complex environmental background are carried out, and the time, position, damage, and so on in the geographical remote sensing images of regional disaster points under the complex environmental background are analyzed. By using the method of parameter analysis and gradient operator operation, a comparison model of geographical remote sensing images of regional disaster points under the complex environmental background is established, and the reliability evaluation of regional comprehensive disaster reduction ability under the complex environmental background is realized according to the method of contrast and detail significance enhancement. The test shows that this method has high accuracy in evaluating regional comprehensive disaster reduction capability under a complex environment, high accuracy in marking the geographical location of regional disaster points under a complex environment, and good fusion performance and reliability of regional comprehensive disaster reduction capability evaluation parameters. [ABSTRACT FROM AUTHOR]

Aiming at the poor anti-jamming effect of traditional fuzzy c-means clustering image segmentation method, a fuzzy c-means clustering image segmentation algorithm based on Hidden Markov model is proposed. Hidden Markov model is used to extract the model state, observation state, initial probability, transition probability and visible symbol probability parameters of fuzzy c-means clustering image. According to these parameters, the image texture is measured, and the image mean is removed to eliminate the influence of brightness. Then the image features are extracted by combining the standard deviation. Through the color space and convert it from RGB model to his model. On the basis of removing the image noise, the image is smoothed to achieve image segmentation. Experimental results show that the algorithm has good segmentation effect, smooth segmentation boundary, little influence of image noise and strong robustness. [ABSTRACT FROM AUTHOR]

Due to low line extraction and matching accuracy, existing point-line fusion visual SLAM（simultaneous localization and mapping） methods have low localization accuracy. This paper improved existing point-line fusion stereo visual SLAM method from multiple dimensions. Firstly, this paper improved the LSD（line segment detection） extraction quality through internal parameters adjustment and length threshold filtering. Secondly, on the basis of geometric constraints, it abstracted the line feature matching as a sparse minimization problem to find best match of line segment. Thirdly, on the basis of image texture score and number of successfully matched features, this paper improved back-end point-line feature fusion strategy by assigning point-line feature weights. Compared with LSD algorithm and LBD（line binary descriptor） matching method, the number of short line segments decreased by 67% on average and matching accuracy increased by 18% on average. Compared with PL-SLAM, localization error dropped by about 15% and 45% respectively on the EuRoC and KITTI datasets. Experiments demonstrate that the proposed method enhances localization accuracy of SLAM system effectively by improving various modules performance of point-line SLAM method in multiple dimensions. [ABSTRACT FROM AUTHOR]

Sea ice has a significant effect on climate change and ship navigation. Hence, it is crucial to draw sea-ice maps that reflect the geographical distribution of different types of sea ice. Many automatic sea-ice classification methods using synthetic aperture radar (SAR) images are based on the polarimetric characteristics or image texture features of sea ice. They either require professional knowledge to design the parameters and features or are sensitive to noise and condition changes. Moreover, ice changes over time are often ignored. In this article, we propose a new SAR sea-ice image classification method based on a combined learning of spatial and temporal features, derived from residual convolutional neural networks (ResNet) and long short-term memory (LSTM) networks. In this way, we achieve automatic and refined classification of sea-ice types. First, we construct a seven-type ice data set according to the Canadian Ice Service ice charts. We extract spatial feature vectors of a time series of sea-ice samples using a trained ResNet network. Then, using the feature vectors as inputs, the LSTM network further learns the variation of the set of sea-ice samples with time. Finally, the extracted high-level features are fed into a softmax classifier to output the most recent ice type. Taking both spatial features and time variation into consideration, our method can achieve a high classification accuracy of 95.7% for seven ice types. Our method can automatically produce more objective sea-ice interpretation maps, allowing detailed sea-ice distribution and improving the efficiency of sea-ice monitoring tasks. [ABSTRACT FROM AUTHOR]

With the continuous development of hyperspectral remote sensing technology, it has been widely used in vegetation mapping. However, sparse vegetation canopy, soil background, and spectral similarity between different types of vegetation are still the main challenges for vegetation types mapping in arid areas. As a result, it is difficult to achieve reliable classification accuracy by using spectral or texture features separately. Generalized Normal Distribution Optimization (GNDO) is a new feature optimization algorithm, with advantages in quality and stability of feature extraction results, comparing to traditional optimization algorithms. But it has not yet been applied to select bands of hyperspectral data. In order to validate the feasibility of combining ZY-1 02D spectral and texture features to classify vegetation types in arid areas, to verify the effectiveness of the GNDO method for bands selection of hyperspectral data, and to explore the effects of feature selection methods and training sample numbers on the classification accuracy of vegetation mapping, different Wrapper Optimization methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and GNDO, were applied to select spectral features for vegetation mapping, taking the area around Zongjia Town, Dulan County, Qinghai Province, China as the research area, then the band selection results of these methods were analyzed. Train sample set containing 30, 50, 100, 150, and 200 pixels per class were used to select bands and to train the classifier. Different methods (ALL (without bands selection), GA, PSO, GWO, GNDO) and different sizes of the trained sample sets (30, 50, 100, 150, and 200 pixels per class) were used to obtain 25 spectral feature-based classification data sets. Simultaneously, 8 texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment) were extracted using the Gray-level co-occurrence matrix (GLCM) method, and selected on basis of distinguishability for vegetation types. Texture features (TEX) were combined with spectral feature-based classification data sets. The random forest classification method was applied to classify vegetation types for the classification data sets, and the classification accuracy of classification data sets was evaluated and compared. The results show that 1) the blue region (400-450 nm), the red edge region (700-750 nm), and the red region (600-650 nm) are the most sensitive to distinguish the vegetation types in the study area; 2) the GNDO200 achieved the highest overall classification accuracy (80.44%) among the spectral feature-based classification data sets, which was better than the classification accuracy (78.86%) using all bands (ALL200); 3) with the increase of training samples, the overall classification accuracy of each classification data set showed an increasing trend, the classification accuracy of different feature selection methods showed different reliance on the number of training samples; 4) image texture features significantly improved the classification accuracy, and the GWO200+TEX dataset had the highest overall classification accuracy (82.86%). This study could verify the potential of the ZY1-02D, the new hyperspectral satellite data, for the classification of vegetation types in arid areas, and provide an idea for the selection of spectral and texture features in hyperspectral vegetation mapping. [ABSTRACT FROM AUTHOR]

Image texture extraction and analysis are fundamental steps in computer vision. In particular, considering the biomedical field, quantitative imaging methods are increasingly gaining importance because they convey scientifically and clinically relevant information for prediction, prognosis, and treatment response assessment. In this context, radiomic approaches are fostering large-scale studies that can have a significant impact in the clinical practice. In this work, we present a novel method, called CHASM (Cuda, HAralick & SoM), which is accelerated on the graphics processing unit (GPU) for quantitative imaging analyses based on Haralick features and on the self-organizing map (SOM). The Haralick features extraction step relies upon the gray-level co-occurrence matrix, which is computationally burdensome on medical images characterized by a high bit depth. The downstream analyses exploit the SOM with the goal of identifying the underlying clusters of pixels in an unsupervised manner. CHASM is conceived to leverage the parallel computation capabilities of modern GPUs. Analyzing ovarian cancer computed tomography images, CHASM achieved up to ∼ 19.5 × and ∼ 37 × speed-up factors for the Haralick feature extraction and for the SOM execution, respectively, compared to the corresponding C++ coded sequential versions. Such computational results point out the potential of GPUs in the clinical research. [ABSTRACT FROM AUTHOR]

A recirculating aquaculture system (RAS) has widely been acknowledged as eco-friendly land-based aquaculture since only a limited amount of water is used in recirculation. There is also much greater potential in modern intensive aquaculture, compared with conventional fish production. A key feature of RAS is the high precise controlling of the water environment in the rearing tank and the feeding of rearing fish. Water environmental parameters (such as water temperature, and oxygen levels) can provide stable and optimal conditions for less stress and better growth during fish living. In addition, fish feeding plays a vital role in RAS, because starvation may impact the organizational structures of fish, whereas, overfeeding may threaten the survival of animals, as well as the water quality. Therefore, it is essential to investigate the strategy of fish feeding in RAS. Currently, the feeding events are mainly implemented by the feeding machine with predetermined and fixed feeding times and quantities, according to the experience rather than the real fish appetite. Thus, it is necessary to evaluate the fish behavior pattern and appetite in the feeding strategy for a higher performance of RAS. In this study, an innovative evaluation of fish feeding behavior was proposed using Fourier spectrum feature extraction and support vector machines (SVM) classification. The degree of surface ripple dispersion was considered to predict the fish feeding behavior, rather than the only movement of fish bodies in conventional cases. Firstly, a high-speed video camera was used to capture the process before and after feeding. The video images were then converted from RGB to HSV space, where the background including the water ripple was subtracted, and the contrast was enhanced via the thresholds of saturation and brightness. Secondly, fast Fourier transform (FFT) was adopted to transform the image information from the spatial to a frequency domain. The fluctuation level of ripples in the image was regarded as an indicator of fish appetite using the amplitudes in the frequency spectrum. Specifically, the frequency spectrum was characterized by a high frequency, short period, and a large sum of amplitudes, when the fishes presented high appetite, and vice versa. Thirdly, the database of feature vectors was generated using the self-developed loop filters with various diameters. Finally, the obtained feature vector was trained to SVM. The feature vector of the SVM model was then optimized, where the optimal kernel width and penalty coefficient were selected to efficiently judge the feeding desire of fish during the cross validation. Moreover, a comparison was conducted to verify the efficiency, among the proposed, optical flow, image texture, shape, and texture features. As such, the original interference condition was transformed into the judgment, indicating the better universality of RAS against the high fish density, unstable water surface ripple, and dark light in the system. A field test also demonstrated that a high accuracy of 99.24% was achieved in judging the feeding behavior of farmed fish in RAS. In practice, it is strongly recommended to take videos of farmed fish as the training data for the classifier of fish feeding behavior. Consequently, the improved RAS can also be incorporated into the feeding machine to supervise the real-time smart and precise feeding events in modern aquaculture. [ABSTRACT FROM AUTHOR]

Skin cancer is the most well‐known disease found in the individuals who are exposed to the Sun's ultra‐violet (UV) radiations. It is identified when skin tissues on the epidermis grow in an uncontrolled manner and appears to be of different colour than the normal skin tissues. This paper focuses on predicting the class of dermascopic images as benign and malignant. A new feature extraction method has been proposed to carry out this work which can extract relevant features from image texture. Local and gradient information from x and y directions of images has been utilized for feature extraction. After that images are classified using machine learning algorithms by using those extracted features. The efficacy of the proposed feature extraction method has been proved by conducting several experiments on the publicly available image dataset 2016 International Skin Imaging Collaboration (ISIC 2016). The classification results obtained by the method are also compared with state‐of‐the‐art feature extraction methods which show that it performs better than others. The evaluation criteria used to obtain the results are accuracy, true positive rate (TPR) and false positive rate (FPR) where TPR and FPR are used for generating receiver operating characteristic curves. [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]

• We first noticed the importance of similar and heterogeneous pixel gradients in feature extraction. • Introducing clustering algorithms into the field of image fusion to improve feature extraction capabilities. • Using source image gradients instead of the clustering base layer of traditional algorithms. Image fusion is performed to merge the information of different source images into one image. In this study, a fusion algorithm based on the pixel mean shift and source image gradient was proposed to make the background of the fused image clear and the target prominent. First, a pixel spatial average kernel function was designed to separate the image texture and the significant target during pixel density migration. In this study, a novel approach is presented that distinguishes itself from other methods in two distinct ways. Firstly, it segregates the texture details of dissimilar pixel regions and captures the texture characteristics of analogous pixel regions. Secondly, by utilizing the discrepancy between the minimum and maximum values of infrared and visible images, the proposed technique incorporates the source graph into the fusion process, thereby improving the precision of the information. Additionally, the feature reconstruction function was designed based on the layer features, which could adaptively concentrate the source image information on one image and further improve the clarity and information retention of the fused image. Finally, the results of the qualitative and quantitative comparison of our proposed method with other state-of-the-art methods available, obtained by applying them to public datasets, demonstrated the advantages of our algorithm. Our results retained more appearance details and significantly more target information. [ABSTRACT FROM AUTHOR]

• Pavement raveling detection based on image processing is proposed. • Texture analysis techniques are employed for feature extraction. • Two scenarios of GLCM and LBP-GLCM are utilized in the feature extraction stage. • XGBoost classifier is employed for the classification of raveling and no-raveling images. • The results of LBP-GLCM feature extraction achieved better performance for raveling detection. The raveling of asphalt pavement is the primary cause of decreasing road safety, comfort, and service life. Because of the asphalt's complex texture, automatic raveling detection from image samples is a challenging operation. In this study, a computer vision technique, based on image texture features, for automatic detection of asphalt pavement raveling is proposed and verified. Two scenarios are taken into account for feature extraction. First, texture features from images are extracted using the traditional GLCM (Gray-Level Co-occurrence Matrix) algorithm. Second, the images are subjected to LBP (Local Binary Pattern) and then GLCM is employed to extract texture features. Utilizing the eXtreme Gradient Boost (XGBoost) technique, two models are built using the mentioned feature extraction scenarios and then compared. The results indicate that compared to the first scenarios prediction performance (Accuracy, Precision, Recall, and F1-Score are all approximately equal to %81), the second feature extraction scenario can offer higher prediction performance (Accuracy, Precision, Recall, and F1-Score are all approximately equal to %97). In order to demonstrate the model's generalizability, a separate dataset is tested. Due to the acceptable performance values for this dataset (with more than %97 in terms of Accuracy, Precision, Recall, and F1-Score), the suggested model can be beneficial for transportation agencies to enhance the efficiency of road inspection activities. [ABSTRACT FROM AUTHOR]

Distinguishing malignant from benign lesions has significant clinical impacts on both early detection and optimal management of those early detections. Convolutional neural network (CNN) has shown great potential in medical imaging applications due to its powerful feature learning capability. However, it is very challenging to obtain pathological ground truth, addition to collected in vivo medical images, to construct objective training labels for feature learning, leading to the difficulty of performing lesion diagnosis. This is contrary to the requirement that CNN algorithms need a large number of datasets for the training. To explore the ability to learn features from small pathologically-proven datasets for differentiation of malignant from benign polyps, we propose a Multi-scale and Multi-level based Gray-level Co-occurrence Matrix CNN (MM-GLCM-CNN). Specifically, instead of inputting the lesions' medical images, the GLCM, which characterizes the lesion heterogeneity in terms of image texture characteristics, is fed into the MM-GLCN-CNN model for the training. This aims to improve feature extraction by introducing multi-scale and multi-level analysis into the construction of lesion texture characteristic descriptors (LTCDs). To learn and fuse multiple sets of LTCDs from small datasets for lesion diagnosis, we further propose an adaptive multi-input CNN learning framework. Furthermore, an Adaptive Weight Network is used to highlight important information and suppress redundant information after the fusion of the LTCDs. We evaluated the performance of MM-GLCM-CNN by the area under the receiver operating characteristic curve (AUC) merit on small private lesion datasets of colon polyps. The AUC score reaches 93.99% with a gain of 1.49% over current state-of-the-art lesion classification methods on the same dataset. This gain indicates the importance of incorporating lesion characteristic heterogeneity for the prediction of lesion malignancy using small pathologically-proven datasets. • We propose an adaptive multi-input CNN model for a polyp classification task. • Our strategy uses texture descriptors to explore the voxel heterogeneity of polyps. • We use multi-scale and multi-level GLCM fusion to enhance classification performance [ABSTRACT FROM AUTHOR]

• Image processing tehnique for water surface velocity prediction. • Temporal feature extraction for dynamic image sequence using XOR operator. • Machine learning method for image texture analysis. Water surface velocity (WSV) is one of the critical parameters in hydrology. The development of non-intrusive measurement of this parameter using cameras is increasing. Traditionally, measuring WSV using a camera utilizes tracking of moving objects on the water surfaces. Recently, another method has emerged that utilizes the movement of water ripples to estimate the WSV. This paper proposes a novel method for estimating WSV based on camera measurements. The authors call this method Temporal Gray-scale Pattern Image (TGPI) since it extracts Gray-scale patterns of pixels of water flow video in the temporal domain using the XOR operator and create new image. The new image pattern formed is then used to predict WSV using predictor. There are four predictors being compared, namely Multiple Input Linear Regression (MILR), Multiple Input Logistic Regression (MILgR), Convolutional Neural Network Regression (CNN-R), and Convolutional Neural Network Classification (CNN-K). CNN-R and CNN-K predict WSV directly from the results of TGPI. Meanwhile, MILR and MILgR predict WSV from five TGPI features. The five features are the Mean and Median of the Histogram, Mean and Median of the Histogram of Oriented Gradient (HOG), and the Maximum Mid-Value of the Fast Fourier Transform (FFT). MILR and CNN-R are predictors for regression problems, so the testing metrics for them are Trend-Line equation and R2 from the predicted WSV and actual values graph. Meanwhile, for MILgR and CNN-K, which are predictors for classification problems, the testing metrics are Confusion Matrix (CM), Accuracy, Precision, Recall, and F1-Score. To test the four methods without distinguishing their predictor types, a 2-dimensional histogram graph is used. The data-set used for training and testing is video footage of water flow with known WSV. The WSV measurement points used in this study are 1.7 m/s, 3.1 m/s, and 4.2 m/s. The video dataset and these three points are generated by the Mini Open Channel Water Flow Simulator (MOCWFS) developed by the author in this study. From the comparison results, it can be seen that the classification type predictor is superior to the regression type. For the regression type predictor, MILR is better than CNN-R. Meanwhile, for the classification type, CNN-K is superior to MILgR. The best accuracy produced by CNN-K is 98.4%. Although there are shortcomings, the TGPI method is quite feasible for predict Water Surface Velocity. [ABSTRACT FROM AUTHOR]

To balance noise reduction and texture details of low-dose computed tomography (LDCT) images during deep learning training, we propose a network combining edge features and a pyramid structure for LDCT denoising (PLEDNet). PLEDNet contains three states: superficial feature acquisition, deep feature acquisition, and feature confluence. First, the superficial feature acquisition stage uses a simple convolutional block to perform feature extraction on the input LDCT image. Second, in the deep feature extraction phase, one branch uses pyramid pooling to extract multiscale features, and the other uses the trainable Laplacian of the Gaussian module to extract edge detail features. Finally, the feature fusion phase uses a fusion attention module to adaptively fuse multiscale and edge features. The results of the experiments demonstrate that the method not only enhances various relative objective indicators but also effectively represses the noise in LDCT images while fully conserving the image texture details, which has great advantages in terms of visual effects. [ABSTRACT FROM AUTHOR]

This paper presents alternative solutions for classifying concrete spall severity based on computer vision approaches. Extreme Gradient Boosting Machine (XGBoost) and Deep Convolutional Neural Network (DCNN) are employed for categorizing image samples into two classes: shallow spall and deep spall. To delineate the properties of a concrete surface subject to spall, texture descriptors including local binary pattern, center symmetric local binary pattern, local ternary pattern, and attractive repulsive center symmetric local binary pattern (ARCS-LBP) are employed as feature extraction methods. In addition, the prediction performance of XGBoost is enhanced by Aquila optimizer metaheuristic. Meanwhile, DCNN is capable of performing image classification directly without the need for texture descriptors. Experimental results with a dataset containing real-world concrete surface images and 20 independent model evaluations point out that the XGBoost optimized by the Aquila metaheuristic and used with ARCS-LBP has achieved an outstanding classification performance with a classification accuracy rate of roughly 99%. • Propose a computer vision method for classifying concrete spall severity. • Extreme Gradient Boosting Machine and deep neural networks are employed. • Use local binary pattern variants as image texture descriptors. • The boosting machine is finetuned by the Aquila optimizer. • The proposed method achieves a high classification accuracy rate = 0.99. [ABSTRACT FROM AUTHOR]

Texture classification has been one of the problem-solving strategies. The feature selection technique is critical for increasing the efficiency of machine learning because it significantly improves the performance of texture classification by removing irrelevant features from the original collection. The majority of methods for selecting features are statistical in nature. Accuracy in database classification is primarily achieved by selecting attributes and simultaneously increasing the classification rate. The task's primary objective is to select the most significant features from the feature database for execution. We use the cluster-based feature selection method to derive features for statistical and structural approaches. The proposed strategy should be implemented in three stages. To begin, the function can be extracted using the local binary pattern (LBP), the gray level co-occurrence matrix, or the Gabor filter. Following that, a modified K-means clustering method is used to select features using four standard distance measurements: Euclidean, Minkowski, Chebychev, and City Block. Finally, benchmark classifiers such as naive bayes (NB), support vector machine (SVM), and K-nearest neighbor (KNN) were used to effectively compute the classification accuracy of the research. The proposed approach achieves accuracy of 99.4328 for KNN, 98.6654 for NB, and 99.6712 for SVM with the highest classification rate. [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]

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]