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• A new feature extraction method based on Discrete Modal Decomposition is proposed. • Modal features can firstly be computed as the DMD coordinates of the image. • Modal features can secondly be computed using the DMD as a local transform process. • Experimental tests show the relevance of the method on a texture classification task, with lower extraction times than state-of-the art methods. Feature extraction is a key step in image processing for pattern recognition and machine learning processes. Its purpose lies in reducing the dimensionality of the input data through the computing of features which accurately describe the original information. In this article, a new feature extraction method based on Discrete Modal Decomposition (DMD) is introduced, to extend the group of space and frequency based features. These new features are called modal features. Initially aiming to decompose a signal into a modal basis built from a vibration mechanics problem, the DMD projection is applied to images in order to extract modal features with two approaches. The first one, called full scale DMD, consists in exploiting directly the decomposition resulting coordinates as features. The second one, called filtering DMD, consists in using the DMD modes as filters to obtain features through a local transformation process. Experiments are performed on image texture classification tasks including several widely used data bases, compared to several classic feature extraction methods. We show that the DMD approach achieves good classification performances, comparable to the state of the art techniques, with a lower extraction time. [ABSTRACT FROM AUTHOR]

This article presents an image texture and hue statistical projection based retrieval. First the image is converted to HSI color model, the gray value of the image extraction, and Robert algorithm to extract the texture, then the image is divided into blocks and extracts the main color block, the main color image blocks are respectively projected in the horizontal and vertical direction of 2, get 2 projection histogram, the 2 projection histograms of the first three order center extraction distance and Robert algorithm as the features of texture, image similarity calculation. Make a very full pave the way for future Canny edge processing algorithm research of image retrieval. [ABSTRACT FROM AUTHOR]

Source camera identification enables forensic investigator to discover the probable source model that are employed to acquire the image under investigation. It is important whenever digital content is presented as a silent witness. In this paper, we present a source camera identification method via image texture features that are extracted from well selected color model and color channel. Except to distinguish source camera models from images whatever they are captured via same or different brand cameras, the main contributions of the proposed method are as follows: (1) It can distinguish imaging device individuals from images even if they are taken by using same brand and model of devices. (2) It is robust for content-preserving manipulations or geometric distortions, such as JEPG compression, adding noise, and rotation and scaling. The experimental results demonstrate that the performance of the proposed method is satisfactory. Compared with the state-of-the-art methods, the proposed method is superior in both detection accuracy and robustness. [ABSTRACT FROM AUTHOR]

Abstract: Motivation: Physical properties of coal such as particle size distribution have a large influence on the stability and operational behavior of fluidized bed reactors and metallurgical furnaces. In particular, the presence of large amounts of “fine” particles invariably has a drastic effect on plant performance as a result of impaired gas permeability characteristics of the coal or ore burden. Therefore, monitoring and control of particle size distribution profiles of such aggregate material on reactor feed streams, such as moving conveyor belts, is critical for predictable operation of these processes. Traditionally, the method of sieve analysis using stock or belt cut samples has been widely used in industry. Unfortunately, the reliability and usefulness of belt cut techniques are constrained by frequency of sampling as well as laboratory analysis turnaround times. For real-time monitoring and control purposes, automated sampling and analysis methods are more desirable. Methods: In this study, the problem of estimating the particle size distribution profile of material on a moving conveyor belt is formulated within a texture classification framework, which has its basis in machine vision and incorporates elements from statistical pattern recognition. Using exemplar images of coal particles taken on a process stream, a set of local features that compactly describes the textural properties of each image are expressed in terms of localized nonlinear features called textons. Representation of image information using textons is primarily motivated by insights from neuroscience research on the optimality of linear oriented basis functions as models of perception in early processing of visual information in the cortex regions of the human brain. Using these representations for different textures, nearest neighbor and support vector machine classification models are subsequently used to classify test images. Results: Using a comprehensive evaluation, it is shown that the use of texton representation obtained from decomposing images with linear oriented basis functions can be sufficiently discriminative compared to the use of the widely used second-order statistical features or features from other baseline models. In particular, model performance obtained with appropriately tuned filters suggest the importance of including shape and spatial structure information in an image representation for texture classification of coal particles. Furthermore, using nonlinear support vector machines rather than nearest neighbor classifiers significantly improved classification performance. A texture classification approach to particle size profile estimation has potential applications in the online monitoring of the proportion of “fines” in coal material on moving conveyor belts. [Copyright &y& Elsevier]

Raveling is one of the critical and pervasive modes of failure observed in asphalt pavement road. Automatic detection of raveling based on image samples is a challenging task due to the complex texture of asphalt pavement. This study constructs and investigates the capability of an image processing based approach for raveling recognition. Image texture based features extracted from statistical properties of color channels and the Gray-Level Co-Occurrence Matrix are employed as input variables to characterize the state of pavement. The Stochastic Gradient Descent Logistic Regression (SGD-LR) is used to classify image samples into two categories of non-raveling and raveling based on a set of extracted features. A SGD-LR based raveling detection program has been developed in Visual C#.NET to facilitate its implementation. Experimental outcome shows that the newly constructed approach can attain a good predictive performance with a classification accuracy rate of roughly 88%. Therefore, this approach can be a helpful tool to assist transportation authorities in the task of surveying asphalt pavement condition. The Stochastic Gradient Descent (SGD) algorithm Graphical user interface of the SGD-LR program used for pavement raveling detection. Unlabelled Image • Image processing based raveling detection is proposed. • Image texture analysis is used in feature extraction phase. • Stochastic Gradient Descent Logistic Regression is used for classification. • The software has been developed in Visual C#.NET. • The program has achieved good detection accuracy of 88.12%. [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]

To ensure the timely detection of safety hazards in transmission lines and to enhance accurately the detection of insulator defects in complicated environments, this study proposes a 3D attention-focused pure convolutional target detection algorithm (CMYOLOv7) based on you only look once v7 (YOLOv7) for defect detection on insulators in complicated environments. Firstly, to address the incapacity of focusing on the target in the backbone feature extraction networks, this study proposes a focused pure convolutional feature extraction module (ConvSimCB) to enhance the extraction and focus capability on insulator defect features. Secondly, to solve the problem of loss of key detail feature information caused by maximum pooling in spatial pyramid pooling module (SPPCSPC), this study proposes a mixed spatial pyramid pooling module (MIXPCSPC) to retain abundant image texture detail information and increase accuracy in detection of tiny insulator defects. Finally, a lightweight generic upsampling operator (CARAFE) is introduced to enhance the feature map resolution to address image distortion caused by the Nearest Neighbor Method of upsampling. This study proposed CMYOLOv7 achieves 98.37% precision, 90.59% recall, 95.68% mean average precision (MAP), and 94% F1 score, higher than YOLOv7 by 0.61%, 8.64%, 5.41%, and 5%. [ABSTRACT FROM AUTHOR]

The fusion of infrared and visible images aims to extract and fuse thermal target information and texture details to the fullest extent possible, enhancing the visual understanding capabilities of images for both humans and computers in complex scenes. However, existing methods have difficulties in preserving the comprehensiveness of source image feature information and enhancing the saliency of image texture information. Therefore, we put forward a novel infrared and visible image fusion algorithm based on global information-enhanced attention network (GIEA). Specifically, we develop an attention-guided Transformer module (AGTM) to make sure the fused images have enough global information. This module combines the convolutional neural network and Transformer to perform adequate feature extraction from shallow to deep layers, and utilize the attention network for multi-level feature-guided learning. Then, we build the contrast enhancement module (CENM), which enhances the feature representation and contrast of the image so that the fused image contains significant texture information. Furthermore, our network is driven to fully preserve the texture and structure details of the source images with a loss function that consists of content loss and total variance loss. Numerous experiments demonstrate that our fusion approach outperforms other fusion approaches in both subjective and objective assessments. • Combining CNN and Transformer to fully extract complementary features. • Designed an attention network for multi-level feature learning. • Designed a contrast enhancement module to enhance feature saliency. [ABSTRACT FROM AUTHOR]

We applied the bag of visual words model for visual texture to a dataset of realistic powder micrograph images drawn from eight closely related particle size distributions. We found that image texture based powder classification performance saturates at 89 ± 3 % with 640 training images (80 images per class). This classification accuracy is comparable to classification using conventional segmentation-based particle size analysis. Furthermore, we found that particle size distributions obtained via watershed segmentation are generally not statistically equivalent to the ground truth particle size distributions, as quantified by the two-sample Kolmogorov-Smirnov test for distribution equivalence. We expect image texture classification methods to outperform particle size analysis for more challenging real-world powder classification tasks by capturing additional information about particle morphology and surface textures, which add complexity to the image segmentation task inherent in particle size distribution estimation. [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]

Highlights • Propose texture features based on a modified version of the local binary pattern (LBP) descriptor. • To improve the texture discrimination ability, the local spatial information of the image is taken into account in the LBP code computation. • For each pixel, the scale parameter and the threshold value of the LBP code are determined using bilateral filter-based multi-scale image analysis. • The effectiveness of the proposed strategy is supported by the analysis of different texture patterns. Graphical abstract Abstract Texture characterization aims at describing the spatial arrangement of local structures within an image. However, mixed pixels that are generally located near boundaries of the regions represent challenge to perform accurate image texture discrimination. To address this problem, this paper proposes a robust discriminating texture features relying on an efficient Local Binary Pattern (LBP) descriptor, where the spatial information within image is taken into account. To determine for each pixel both a proper scale parameter and a threshold value to compute the LBP code, an efficient way relying on bilateral filter-based multi-scale image analysis is used. First, the difference of Gaussian operator is used to determine the corresponding scale. Second, key points based-approach is used to identify the threshold value of each pixel. This provides the ability to deal with mixed pixels. Then, LBP code is computed to characterize the texture information for each pixel. Experimental results, using both synthetic and real images, show that the proposed appropriate-scale-threshold selection strategy demonstrates a significant improvement in texture discrimination ability. [ABSTRACT FROM AUTHOR]

Motivated by researching new image texture modeling that improves state-of-the-art LBP variants and non-LBP descriptors, this paper proposes a novel approach for constructing local image descriptors, which are suitable for histogram based image representation. Instead of heuristic code constructions, the proposed approach is based on local concave-and-convex characteristics, which have high ability to extract discriminative and stable texture representation. Different from the majority of descriptors that only encode relationships between the pixels in doublets around central pixel (within 3  ×  3 neighborhood), the proposed approach encodes relationships between the pixels in triplets by including the central pixel in the modeling. We build two distinct descriptors by dividing local features into two distinct groups, i.e., local concave and convex microstructure patterns (LCvMSP and LCxMSP), according to relationships between the pixels inside the triplets, formed along closed path around the central pixel of a 3 × 3-grayscale image patch. To make the descriptors more insensitive to noise and invariant to monotonic gray scale transformation, two supplementary triplets are added in the modeling. These triplets are formed using the central pixel and four virtual pixels set to the median of the grey-scale values of the 3 × 3 neighbourhood and the whole image and the average local and global gray levels respectively. The histograms obtained from the single scale descriptors LCvMSP and LCxMSP are concatenated together to build multi-scale histogram feature vector referred to as local concave-and-convex micro-structure pattern (LCCMSP), that is expected to better represent salient local texture structure. We evaluated the effectiveness of the proposed methods on thirteen challenging representative widely-used texture datasets, and found that the proposed LCvMSP, LCxMSP and LCCMSP operators achieve performances that are competitive or better than a large number of recent most promising state-of- the-art LBP variants and non-LBP descriptors. Statistical comparison based on Wilcoxon signed rank test demonstrated that the proposed methods are the top three over all the tested datasets. [ABSTRACT FROM AUTHOR]

• Asphalt paving segregation detection using image texture features. • Texture feature extraction algorithm combining uniform pattern LBP and GLCM. • Asphalt paving segregation detection based on uniform pattern LBP-GLCM. • The support vector machine classifier is used to detection whether paving segregation occurs. The segregation of asphalt pavement is the main reason for the decrease of safety, comfort and actual service life of the road, and the paving segregation is the main inducement for asphalt pavements segregation. Thus, a kind of effective paving segregation detection method can reduce the occurrence of asphalt pavement segregation. The traditional asphalt segregation detection methods are mainly divided into contact detection and non-contact detection. The contact detection method can only detect the segregation of pavement after paving or in use, and the non-contact detection method is also generally limited by the noise and expensive equipment. In recent years, the rapid development of image processing technology has provided a new research direction for asphalt paving segregation detection, but the accuracy and efficiency of the existing image-based asphalt paving segregation detection methods are insufficient. In order to solve these problems, this paper proposes an asphalt paving segregation detection method based on image texture features. Firstly, based on the traditional algorithms LBP (Local Binary Pattern) and GLCM (Gray-level Co-occurrence Matrix), a new texture feature extraction algorithm uniform pattern LBP-GLCM is proposed. Secondly, a detection method based on uniform pattern LBP-GLCM in combination with SVM (Support Vector Machine) is proposed. Then, the detection method proposed is validated using Kylbery texture dataset, the result show that this detection methods has great accuracy and efficiency in the classification of targets with similar texture features, it also means the texture feature extract method based on uniform pattern LBP-GLCM can combine the advantages of LBP and GLCM to achieve improvement of feature extraction's performance and efficiency. Finally, the detection method is applied to the diagnosis of asphalt paving segregation, and the accuracy of diagnosis achieves 94%. Compared with the existing algorithms, detection method based on uniform pattern LBP-GLCM has higher diagnostic accuracy and efficiency. Specifically, detection method with uniform pattern LBP-GLCM can improve accuracy in comparison with single asphalt pavement paving segregation detection method, and it can improve efficiency in comparison with existing hybrid asphalt pavement paving segregation detection method. The results of this study can potentially be used for real-time detection of asphalt paving segregation. [ABSTRACT FROM AUTHOR]

Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic EEG records. We aim to achieve this with the minimum supervision from the neurologist. To accomplish this objective, first a novel feature extraction method is proposed based on the mapping of EEG signals into two dimensional space, resulting into a texture image. The texture image is constructed by mapping and scaling EEG signals and their associated frequency sub-bands into the gray-level image domain. Image texture analysis using gray level co-occurrence matrix (GLCM) is then applied in order to extract multivariate features which are able to differentiate between seizure and seizure-free events. To evaluate the discriminative power of the proposed feature extraction method, a comparative study is performed, against other dedicated feature extraction methods. The comparative performance evaluations show that the proposed feature extraction method can outperform other state-of-art feature extraction methods with a low computational cost. With a training rate of 25%, the overall sensitivity of 70.19% and specificity of 97.74% are achieved in the classification of over 163 h of EEG records using support vector machine (SVM) classifiers with linear kernels and trained by the stochastic gradient descent (SGD) algorithm. [ABSTRACT FROM AUTHOR]

With recent improvements in instrumentation and computer infrastructure, machine vision technologies have produced innovative approaches for controlling and monitoring mineral flotation process. In order to efficiently analyze froth image, froth texture is used to accurately and rapidly extract froth characteristics based on the statistics of image pixels. The characterization and identification of texture require a method that can express the context surrounding each pixel by combining local and global texture characteristics. To extract the distinctive froth texture features in different production states, a novel complex networks-based texture extraction and classification method for froth imaging is proposed in this paper. A network model is constructed by expressing pixels as network nodes and similarities between pixels as network links. This method automatically sets the optimal algorithm parameters for the complex network modeling of the froth images according to bubble sizes by using the Minkowski distance. Energy and entropy measurements are used to quantify the properties of the connectivity and topology of the froth-image network model. Copper froth images at different production states extracted from the flotation monitoring system in a flotation plant are used to test the froth-image network model. The experimental results show that the proposed method accurately describes the froth image texture and also robustly classifies the different production states. [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]

Fatty Liver Disease (FLD) is a progressively prevalent disease that is present in about 15% of the world population. Normally benign and reversible if detected at an early stage, FLD, if left undetected and untreated, can progress to an irreversible advanced liver disease, such as fibrosis, cirrhosis, liver cancer and liver failure, which can cause death. Ultrasound (US) is the most widely used modality to detect FLD. However, the accuracy of US-based diagnosis depends on both the training and expertise of the radiologist. US-based Computer Aided Diagnosis (CAD) techniques for FLD detection can improve accuracy, speed and objectiveness of the diagnosis, and thereby, reduce operator dependability. In this paper, we first review the advantages and limitations of different diagnostic methods which are currently available to detect FLD. We then review the state-of-the-art US-based CAD techniques that utilize a range of image texture based features like entropy, Local Binary Pattern (LBP), Haralick textures and run length matrix in several automated decision making algorithms. These classification algorithms are trained using the features extracted from the patient data in order for them to learn the relationship between the features and the end-result (FLD present or absent). Subsequently, features from a new patient are input to these trained classifiers to determine if he/she has FLD. Due to the use of such automated systems, the inter-observer variability and the subjectivity of associated with reading images by radiologists are eliminated, resulting in a more accurate and quick diagnosis for the patient and time and cost savings for both the patient and the hospital. [ABSTRACT FROM AUTHOR]

Abstract: Recent approaches to crater detection have been inspired by face detection’s use of gray-scale texture features. Using gray-scale texture features for supervised machine learning crater detection algorithms provides better classification of craters in planetary images than previous methods. When using Haar features it is typical to generate thousands of numerical values from each candidate crater image. This magnitude of image features to extract and consider can spell disaster when the application is an entire planetary surface. One solution is to reduce the number of features extracted and considered in order to increase accuracy as well as speed. Feature subset selection provides the operational classifiers with a concise and denoised set of features by reducing irrelevant and redundant features. Feature subset selection is known to be NP-hard. To provide an efficient suboptimal solution, four genetic algorithms are proposed to use greedy selection, weighted random selection, and simulated annealing to distinguish discriminate features from indiscriminate features. Inspired by analysis regarding the relationship between subset size and accuracy, a squeezing algorithm is presented to shrink the genetic algorithm’s chromosome cardinality during the genetic iterations. A significant increase in the classification performance of a Bayesian classifier in crater detection using image texture features is observed. [Copyright &y& Elsevier]

Abstract: As an emerging biometric technology, palmprint recognition has been extensively researched due to its easy collection, user friendliness, high verification accuracy and reliability. Blanket dimension is a commonly used fractal dimension and has the multi-resolution characteristics with which the image texture information can be better extracted. In this work, palmprint recognition with blanket dimension and its expansions was investigated. The efficiencies of horizontally and vertically expanded blanket dimensions for extracting the directional feature of palmprint were compared, and a palmprint recognition algorithm based on horizontally expanded blanket dimension (HEBD) was proposed according to the comparison results. Furthermore, a multi-scale HEBD (MHEBD) algorithm for palmprint recognition was also presented, and the MHEBD was demonstrated to be more effective than the single-scale HEBD for feature extraction. The algorithm was evaluated on Hong Kong Polytechnic University (PolyU) database (v2) and CASIA database. The experimental results indicate that the multi-scale HEBD can extract the palmprint features effectively and efficiently with a high recognition rate and less processing time. [Copyright &y& Elsevier]

Abstract: Extraction of the flooded areas from synthetic aperture radar (SAR) and especially TerraSAR-X data is one of the most challenging tasks in the flood management and planning. SAR data due to its high spatial resolution and its capability of all weather conditions makes a proper choice for tropical countries. Texture is considered as an effective factor in distinguishing the classes especially in SAR imagery which records the backscatters that carry information of kind, direction, heterogeneity and relationship of the features. This paper put forward a computer program for texture analysis for high resolution radar data. Texture analysis program is introduced and discussed using the gray-level co-occurrence matrix (GLCM). To demonstrate the ability and correctness of this program, a test subset of TerraSAR-X imagery from Terengganu area, Malaysia was analyzed and pixel-based and object-based classification were attempted. The thematic maps derived by pixel-based method could not achieve acceptable visual interpretation and for that reason no accuracy assessment was performed on them. The overall accuracy achieved by object-based method was 83.63% with kappa coefficient of 0.8. Results on image texture classification showed that the proposed program is capable for texture analysis in TerraSAR-X image and the obtained textural analysis resulted in high classification accuracy. The proposed texture analysis program can be used in many applications such as land use/cover (LULC) mapping, hazard studies and many other applications. [Copyright &y& Elsevier]

Abstract: Coronary Artery Disease (CAD), caused by the buildup of plaque on the inside of the coronary arteries, has a high mortality rate. To efficiently detect this condition from echocardiography images, with lesser inter-observer variability and visual interpretation errors, computer based data mining techniques may be exploited. We have developed and presented one such technique in this paper for the classification of normal and CAD affected cases. A multitude of grayscale features (fractal dimension, entropies based on the higher order spectra, features based on image texture and local binary patterns, and wavelet based features) were extracted from echocardiography images belonging to a huge database of 400 normal cases and 400 CAD patients. Only the features that had good discriminating capability were selected using t-test. Several combinations of the resultant significant features were used to evaluate many supervised classifiers to find the combination that presents a good accuracy. We observed that the Gaussian Mixture Model (GMM) classifier trained with a feature subset made up of nine significant features presented the highest accuracy, sensitivity, specificity, and positive predictive value of 100%. We have also developed a novel, highly discriminative HeartIndex, which is a single number that is calculated from the combination of the features, in order to objectively classify the images from either of the two classes. Such an index allows for an easier implementation of the technique for automated CAD detection in the computers in hospitals and clinics. [Copyright &y& Elsevier]

Abstract: Image processing algorithms can be used in computer-aided diagnosis systems to extract features directly from digitized mammograms. Typically, two classes of features are extracted from mammograms with these algorithms, namely morphological and non-morphological features. Image texture analysis is an important technique that represents gray level properties of images used to describe non-morphological features. This technique has shown to be a promising technique in analyzing mammographic lesions caused by masses. In this paper, we evaluate texture classification using features derived from co-occurrence matrices, wavelet and ridgelet transforms of mammographic images. In particular, we propose a false positive reduction in computer-aided detection of masses. The data set consisted of 120 cranio-caudal mammograms, half containing a mass, rated as abnormal images, and half with no lesions. The following texture descriptors were then calculated to analyze the regions of interest (ROIs) texture patterns: entropy, energy, sum average, sum variance, and cluster tendency. To select the best set of features for each method, we applied a genetic algorithm (GA). In the ROIs classification stage, we used the Random Forest algorithm, a data mining technique that separates the data into non-overlapping segments. Experimental results showed that the best classification rates were obtained with the wavelet-based feature extraction using GA for selection of the most relevant features, giving an AUC=0.90. [Copyright &y& Elsevier]

Abstract: One of the principal causes for image quality degradation is blur. This frequent phenomenon is usually a result of misfocused optics or camera motion, and it is very difficult to undo. Beyond the impaired visual quality, blurring causes problems to computer vision algorithms. In this paper, we present a simple yet powerful image descriptor, which is robust against the most common image blurs. The proposed method is based on quantizing the phase information of the local Fourier transform and it can be used to characterize the underlying image texture. We show how to construct several variants of our descriptor by varying the technique for local phase estimation and utilizing the proposed data decorrelation scheme. The descriptors are assessed in texture and face recognition experiments, and the results are compared with several state-of-the-art methods. The difference to the baseline is considerable in the case of blurred images, but also with sharp images our method gives a highly competitive performance. [Copyright &y& Elsevier]

Abstract: Beef longissimus dorsi colour, marbling fat and surface texture are long established properties that are used in some countries by expert graders to classify beef carcasses, with subjective and inconsistent decision. As a computer vision system can deliver objective and consistent decisions rapidly and is capable of handling a greater variety of image features, attempts have been made to develop computerised predictions of eating quality based on these and other properties but have failed to adequately model the variation in eating quality. Therefore, in this study, examination of the ribeye at high magnification and consideration of a broad range of colour and marbling fat features was used to attempt to provide better information on beef eating quality. Wavelets were used to describe the image texture of the beef surface at high magnification rather than classical methods such as run lengths, difference histograms and co-occurrence matrices. Sensory panel and Instron analyses were performed on duplicate steaks to measure the quality of the beef. Using the classical statistical method of partial least squares regression (PLSR) it was possible to model a very high proportion of the variation in eating quality (r 2 =0.88 for sensory overall acceptability and r 2 =0.85 for 7-day WBS). Addition of non-linear texture terms to the models gave some improvements. [Copyright &y& Elsevier]

• 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 function of the thyroid gland could be disrupted if it produces too much or too little hormones. • The visual interpretation of the ultrasound thyroid images is challenging and time-consuming. • Bayesian network inference can estimate textural features with significant conditional dependencies for classification. • A novel texture feature engineering algorithmic model can differentiate the benign and malignant thyroid nodules. The thyroid is the chief hormonal gland that controls the growth, metabolism, and maturation of the body. However, the function of the thyroid gland could be disrupted if it produces too much or too little hormones. Furthermore, there could be abnormal growth in thyroid cell tissue, leading to the formation of a benign or malignant thyroid lesion. Ultrasound is a typical non-invasive diagnosis approach to check for cancerous thyroid lesions. However, the visual interpretation of the ultrasound thyroid images is challenging and time-consuming. Hence, a feature engineering model is proposed to overcome these challenges. We propose to transform image pixel intensity values into high dimensional structured data set before fitting a Regression analysis framework to estimate kernel parameters for an image filter model. We then adopt a Bayesian network inference to estimate a subset for the textural features with a significant conditional dependency in the classification of thyroid lesions. The analysis of the proposed feature engineering model showed that the classification performance had an overall significant improvement over other image filter models. We achieve 96.00% classification accuracy with a sensitivity and specificity of 99.64% and 90.23% respectively for a filter size of 13 × 13. The analysis of results indicate that the diagnosis of ultrasound images thyroid nodules is significantly boosts by adaptively learning filter parameters for feature engineering model. [ABSTRACT FROM AUTHOR]

In the process of chemical production and biopharmaceutical, with the complex reaction, the products will overlap or adhere to each other. Effectively distinguishing the overlap and adhesion of crystals is of great significance for the statistics of different morphological characteristics such as the number and size of crystals. This paper proposes a crystal texture recognition system based on image analysis, which mainly includes image pre-processing, feature extraction and texture classification. Firstly, the crystal images are pre-processed to eliminate the influence of water droplets, particle shadows and uneven illumination. Secondly, the Improved-Basic Gray Level Aura matrix (I-BGLAM) is used to extract texture features of the crystals to determine the focus state of crystals. Finally, the texture features are classified by back propagation neural network (BPNN) to effectively distinguish agglomerates and pseudo-agglomerates. The case study and experimental results of cooling crystallization of l-glutamic acid show that the texture recognition system can effectively distinguish the adhesion and overlap of crystals, and effectively analyze the agglomerates, and has good experimental accuracy. • We propose an image preprocessing method which can effectively remove various interferences caused by invasive imaging system. • We use image texture to analyze the focus state of the crystals and to determine the adhesion and overlap of the crystals. • We propose using BPNN to classify the texture and determine the crystal type. • We teste the discriminating effect of various feature extractors, and finally used I-BGLAM to identify agglomerations according to the experimental results. [ABSTRACT FROM AUTHOR]

As diabetic maculopathy (DM) is a prevalent cause of blindness in the world, it is increasingly important to use automated techniques for the early detection of the disease. In this paper, we propose a decision system to classify DM fundus images into normal, clinically significant macular edema (CMSE), and non-clinically significant macular edema (non-CMSE) classes. The objective of the proposed decisionsystem is three fold namely, to automatically extract textural features (both region specific and global), to effectively choose subset of discriminatory features, and to classify DM fundus images to their corresponding class of disease severity. The system uses a gamut of textural features and an ensemble classifier derived from four popular classifiers such as the hidden naïve Bayes, naïve Bayes, sequential minimal optimization (SMO), and the tree-based |48 classifiers. We achieved an average classification accuracy of 96.7% using five-fold cross validation. [ABSTRACT FROM AUTHOR]

Abstract: Surface texture is an important characteristic of foods, as well as color, shape, consistency and taste. It plays an important role in consumers' decision and it can affect the properties of a product during its preparation. This work shows the ability of three different image analysis techniques to characterize the surface texture of three Italian pasta samples. The first method is based on the evaluation of Heterogeneity (HTG); the second on the gray level co-occurrence matrix (GLCM) and Haralic statistics; the third, the angle measure technique (AMT), is based on image multivariate feature extraction. The results obtained showed that it is possible to highlight differences in the surface aspect of pasta samples both before and after cooking, and that it is also possible to correlate them to some of their chemical–physical characteristics (e.g., total starch and protein contents, solids lost in the cooking water, pasta adhesiveness; r>0.6, p<0.05). A partial least square discriminant analysis (PLS-DA) applied on GLCM and AMT results allowed the classification of the different pasta samples only on the basis of their surface texture features (sensitivity>0.963; specificity>0.648). [Copyright &y& Elsevier]