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• 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]

• 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]

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]

• 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]