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The geotechnical characterization of the subsurface is a key requirement for most soil investigations, incl. those for reclaiming landfills and waste dumps associated with mining operations. New sensor technology, combined with intelligent analysis algorithms, allow for a faster and less expensive acquisition of the necessary information without loss of data quality. The use of advanced technologies to support and back up common site investigation techniques, such as cone penetration testing (CPT), can enhance the underground characterization process. This study aims to investigate the possibilities of image analysis for material recognition to advance the geotechnical characterization process. The grey level co-occurrence matrix (GLCM) image processing technique is used in a wide range of study fields to estimate textures, patterns and structure anomalies. This method was adjusted and applied to process the video recorded during a CPT sounding, in order to distinguish soil types by its changing surface characteristics. From the results of the video processing, it is evident that the GLCM technique can identify transitions in soil types that were captured in the video recording. This enables the prospect of image analysis not just for soil investigations, but also for monitoring of the conveyor belt in the mining field, to allow for efficient preliminary decision making, material documentation and quality control by providing information in a cost effective and efficient manner.

This paper focuses on the automated extraction of textural features from segmented Sentinel-1A Synthetic Aperture Radar (SAR) imagery, which was captured on 25th August 2017. Textural properties play a pivotal role in discerning regions of interest in satellite imagery, essential for precise classification in diverse applications, including remote sensing, medical image analysis, biometric analysis, and document image analysis. To achieve this, the Local Adaptive Threshold (LAT) technique was employed for segmenting the foreground and background areas of the pre-processed vertical transmit-vertical receive (VV) polarized Sentinel-1A SAR data. The approach relies on second-order statistics, specifically co-occurrence measures, which evaluate the relationships between pairs of pixels within their local neighbourhood's in the input image. Four fundamental second-order statistical measures, namely correlation, energy, homogeneity, and contrast, were computed using the Grey Level Co-occurrence Matrix (GLCM). The GLCM was generated by quantizing the foreground region of the segmented grey-scale image into different levels, including 8, 16, and 64 grey levels, considering two inter-pixel distances, d=1 and d=2, and adopting an omni-directional orientation (θ = 00, 450, 900, 1350).The results underscore the effectiveness of the GLCM-based approach in computing second-order statistical texture measures from the segmented SAR image. Notably, the findings highlight that quantizing the image to Ng=8 with d=1 and considering omni-directional statistical measures (θ = 00, 450, 900, 1350) significantly enhances performance. [ABSTRACT FROM AUTHOR]

As prior information for precise nitrogen fertilization management, plant nitrogen content (PNC), which is obtained timely and accurately through a low-cost method, is of great significance for national grain security and sustainable social development. In this study, the potential of the low-cost unmanned aerial vehicle (UAV) RGB system was investigated for the rapid and accurate estimation of winter wheat PNC across the growing season. Specifically, texture features were utilized as complements to the commonly used spectral information. Five machine learning regression algorithms, including support vector machines (SVMs), classification and regression trees, artificial neural networks, K-nearest neighbors, and random forests, were employed to establish the bridge between UAV RGB image-derived features and ground-truth PNC, with multivariate linear regression serving as the reference. The results show that both spectral and texture features had significant correlations with ground-truth PNC, indicating the potential of low-cost UAV RGB images to estimate winter wheat PNC. The H channel, S4O6, and R_SE and R_EN had the highest correlation among the spectral indices, Gabor texture features, and grey level co-occurrence matrix texture features, with absolute Pearson's correlation coefficient values of 0.63, 0.54, and 0.69, respectively. When the texture features were used together with spectral indices, the PNC estimation accuracy was enhanced, with the root mean square error (RMSE) decreasing from 2.56 to 2.24 g/kg, for instance, when using the SVM regression algorithm. The SVM regression algorithm with validation achieved the highest estimation accuracy, with a coefficient of determination (R2) of 0.62 and an RMSE of 2.15 g/kg based on the optimal feature combination of B_CON, B_M, G_DIS, H, NGBDI, R_EN, R_M, R_SE, S3O7, and VEG. Overall, this study demonstrated that the low-cost UAV RGB system could be successfully used to map the PNC of winter wheat across the growing season. [ABSTRACT FROM AUTHOR]

The geotechnical characterization of the subsurface is a key requirement for most soil investigations, incl. those for reclaiming landfills and waste dumps associated with mining operations. New sensor technology, combined with intelligent analysis algorithms, allow for a faster and less expensive acquisition of the necessary information without loss of data quality. The use of advanced technologies to support and back up common site investigation techniques, such as cone penetration testing (CPT), can enhance the underground characterization process. This study aims to investigate the possibilities of image analysis for material recognition to advance the geotechnical characterization process. The grey level co-occurrence matrix (GLCM) image processing technique is used in a wide range of study fields to estimate textures, patterns and structure anomalies. This method was adjusted and applied to process the video recorded during a CPT sounding, in order to distinguish soil types by its changing surface characteristics. From the results of the video processing, it is evident that the GLCM technique can identify transitions in soil types that were captured in the video recording. This enables the prospect of image analysis not just for soil investigations, but also for monitoring of the conveyor belt in the mining field, to allow for efficient preliminary decision making, material documentation and quality control by providing information in a cost effective and efficient manner. [ABSTRACT FROM AUTHOR]

Background: Posterior capsular opacification (PCO) is a common complication following cataract surgery that leads to visual disturbances and decreased quality of vision. The aim of our study was to employ a machine‐learning methodology to characterize and validate enhancements applied to the grey‐level co‐occurrence matrix (GLCM) while assessing its validity in comparison to clinical evaluations for evaluating PCO. Methods: One hundred patients diagnosed with age‐related cataracts who were scheduled for phacoemulsification surgery were included in the study. Following mydriasis, anterior segment photographs were captured using a high‐resolution photographic system. The GLCM was utilized as the feature extractor, and a supported vector machine as the regressor. Three variations, namely, GLCM, GLCM+C (+axial information), and GLCM+V (+regional voting), were analyzed. The reference value for regression was determined by averaging clinical scores obtained through subjective analysis. The relationships between the predicted PCO outcome scores and the ground truth were assessed using Pearson correlation analysis and a Bland–Altman plot, while agreement between them was assessed through the Bland–Altman plot. Results: Relative to the ground truth, the GLCM, GLCM+C, and GLCM+V methods exhibited correlation coefficients of 0.706, 0.768, and 0.829, respectively. The relationship between the PCO score predicted by the GLCM+V method and the ground truth was statistically significant (p < 0.001). Furthermore, the GLCM+V method demonstrated competitive performance comparable to that of two experienced clinicians (r = 0.825, 0.843) and superior to that of two junior clinicians (r = 0.786, 0.756). Notably, a high level of agreement was observed between predictions and the ground truth, without significant evidence of proportional bias (p > 0.05). Conclusions: Overall, our findings suggest that a machine‐learning approach incorporating the GLCM, specifically the GLCM+V method, holds promise as an objective and reliable tool for assessing PCO progression. Further studies in larger patient cohorts are warranted to validate these findings and explore their potential clinical applications. [ABSTRACT FROM AUTHOR]

Selecting the appropriate unmanned aerial vehicle flight height is beneficial for increasing the monitoring efficiency. We firstly used an unmanned aerial vehicle to explore the scale effect on monitoring rice aboveground biomass. The results confirmed the feasibility of using vegetation indices and textures from hyperspectral images to improve the estimations at different spatial resolutions. The monitoring accuracy of combining vegetation indices and textures was the highest, and exhibited a decreasing trend as the spatial resolution decreased with the greatest accuracy appearing at 13 cm. Two new concepts were proposed: “appropriate monitoring scale domain” to define the range of spatial resolution where the monitoring accuracy was less affected by scale effect, and “appropriate monitoring scale threshold” to define the spatial resolution where accuracy dropped noticeably. The appropriate monitoring scale domains varied at different growth stages and the appropriate monitoring scale thresholds of using vegetation indices and textures were lower than those using textures: 39 cm, 52 cm, and 65 cm at the pre-heading, post-heading, and entire growth stages, respectively when using textures, and 52 cm, 65 cm, and 78 cm at the corresponding growth stages when combining vegetation indices and textures. In terms of aboveground biomass level, growth stage and error value, the relatively lower aboveground biomass levels, earlier growth stages of the multi-temporal models, and overestimations were more likely to yield notable accuracy changes when the spatial resolution converted to lower level on both sides of appropriate monitoring scale threshold. Vegetation indices containing red-edge or near-infrared bands were effective for estimation. Yellow/green band textures and vegetation indices containing green bands with near-infrared/red-edge bands also obtained inspiring performances. MEA was indispensable in estimation while more diverse textures were incorporated into the models of the entire growth stages and models established at lower spatial resolutions. These findings are essential for understanding the scale effect in estimating rice aboveground biomass, facilitating efficient monitoring at field scale.

In this article, local monotonic pattern (LMP) paired with grey level co‐occurrence matrix (GLCM) methods are suggested to identify facial emotions with a high identification rate even when the face pictures are rotated. The proposed method extracts image features using the properties of the LMP algorithm and features extracted from the GLCM, which are then fed into the Support Vector Machine (SVM) process that reduces the dimensionality of the features vector and classifies the output into different facial expressions or emotions. The SVM performance rate is then compared to the K‐nearest neighbour approach (KNN) to see which method produces the best facial emotion identification and categorization. The study identified facial emotions in the images using advanced algorithms of GLCM and LMP models to be compared. As a result, the accuracy of SVM and KNN was utilized to determine the method's usefulness in classification using the application of MATLAB. A result of more than 93% was achieved using the SVM method compared with 89.4% using the KNN for the recognition process. The study also demonstrated that this approach would lead to more classification outcomes if the LMP and GLCM are combined with an edge‐based technique yielding a new method that is more efficient and more effective. [ABSTRACT FROM AUTHOR]

With the launch of the Sentinel-1 mission, for the first time, multitemporal and dual-polarization C-band SAR data with a short revisit time is freely available. How can we use this data to generate accurate vegetation cover maps on a local scale? Our main objective was to assess the use of multitemporal C-Band Sentinel-1 data to generate wetland vegetation maps. We considered a portion of the Lower Delta of the Paraná River wetland (Argentina). Seventy-four images were acquired and 90 datasets were created with them, each one addressing a combination of seasons (spring, autumn, winter, summer, complete set), polarization (VV, HV, both), and texture measures (included or not). For each dataset, a Random Forest classifier was trained. Then, the kappa index values (κ) obtained by the 90 classifications made were compared. Considering the datasets formed by the intensity values, for the winter dates the achieved kappa index values (κ) were higher than 0.8, while all summer datasets achieved κ up to 0.76. Including feature textures based on the GLCM showed improvements in the classifications: for the summer datasets, the κ improvements were between 9% and 22% and for winter datasets improvements were up to 15%. Our results suggest that for the analyzed context, winter is the most informative season. Moreover, for dates associated with high biomass, the textures provide complementary information.

As prior information for precise nitrogen fertilization management, plant nitrogen content (PNC), which is obtained timely and accurately through a low-cost method, is of great significance for national grain security and sustainable social development. In this study, the potential of the low-cost unmanned aerial vehicle (UAV) RGB system was investigated for the rapid and accurate estimation of winter wheat PNC across the growing season. Specifically, texture features were utilized as complements to the commonly used spectral information. Five machine learning regression algorithms, including support vector machines (SVMs), classification and regression trees, artificial neural networks, K-nearest neighbors, and random forests, were employed to establish the bridge between UAV RGB image-derived features and ground-truth PNC, with multivariate linear regression serving as the reference. The results show that both spectral and texture features had significant correlations with ground-truth PNC, indicating the potential of low-cost UAV RGB images to estimate winter wheat PNC. The H channel, S4O6, and R_SE and R_EN had the highest correlation among the spectral indices, Gabor texture features, and grey level co-occurrence matrix texture features, with absolute Pearson’s correlation coefficient values of 0.63, 0.54, and 0.69, respectively. When the texture features were used together with spectral indices, the PNC estimation accuracy was enhanced, with the root mean square error (RMSE) decreasing from 2.56 to 2.24 g/kg, for instance, when using the SVM regression algorithm. The SVM regression algorithm with validation achieved the highest estimation accuracy, with a coefficient of determination (R2) of 0.62 and an RMSE of 2.15 g/kg based on the optimal feature combination of B_CON, B_M, G_DIS, H, NGBDI, R_EN, R_M, R_SE, S3O7, and VEG. Overall, this study demonstrated that the low-cost UAV RGB system could be successfully used to map the PNC of winter wheat across the growing season.

Online weld joint inspection by non-destructive trial is significantly necessitated for modern joining industries. The joining of dissimilar materials as compared to similar materials desirable from automotive, railways to naval trades. The weld imperfection examination is a key portion of their trial as the physical examination may be ambiguous to proper validations and key to improper reorganization. Due to this non-destructive testing gained popularity through dominance in examinations and reliable in confirming the part excellence. Therefore, to achieve defects free weld author presented an independent technique for the detection and classification of multiform weld joint flaws specifically containing a crack, undercut, gas pores, porosity, slag, warm holes, lack of penetration, and non-defects in X-ray images by artificial neural network and support vector machine to approve their high-performance accurateness. The proposed technique is combined with primarily four parts. In the first part pre-process of the weld image by lessening noises achieved protective boundaries through median filtering and brightness gradient attained by a four-sided formed histogram using scattering of grey levels to larger choice. Moreover, the second part attained comprehensive and mostly continuous boundaries of weld image by canny edge operator as linked to further edge operators. The third part pulls out separated entities and is specified as prime data classifier by grey level co-occurrence matrix using ten surface features. Lastly, for speedy detection and classification of weld imperfections achieved by artificial neural network and support vector machine and established their accuracy performance of 98.75% and 96.25% via confusion matrix. With the proposed independent techniques for the detection and classification of X-ray images achieved supreme computation period without disturbing the entire correctness of features selection and offered wide-ranging state-of-the-art techniques with enhanced outcomes. [ABSTRACT FROM AUTHOR]

Shot boundary detection is the fundamental technique that plays an important role in a variety of video processing tasks such as summarization, retrieval, object tracking, and so on. This technique involves segmenting a video sequence into shots, each of which is a sequence of interrelated temporal frames. This paper introduces two methods, where the first is for detecting the cut shot boundary via employing visual hybrid features, while the second method is to compare between them. This enhances the effectiveness of the performance of detecting the shot by selecting the strongest features. The first method was performed by utilizing hybrid features, which included statistics histogram of hue-saturation-value color space and grey level co-occurrence matrix. The second method was performed by utilizing hybrid features that include discrete wavelet transform and grey level co-occurrence matrix. The frame size decreased. This process had the advantage of reducing the computation time. Also used local adaptive thresholds, which enhanced the method’s performance. The tested videos were obtained from the BBC archive, which included BBC Learning English and BBC News. Experimental results have indicated that the second method has achieved (97.618%) accuracy performance, which was higher than the first and other methods using evaluation metrics. [ABSTRACT FROM AUTHOR]

Agriculture has become the main sources of the income for many developed countries. The productivity in agriculture can be affected by various diseases present in the plant due to climatic conditions. The key step to improve the productivity of crops are to detect the disease at the preliminary stage. Automation becomes the best solution for this because it is more difficult to observe the disorders in plants parts. For that an image of affected plant leaf is acquired and segments the affected portion and to recognize the disease by using image processing and computer vision and machine learning techniques. The extracted features from the segmented portion are descripted using Global and Local Visual descriptors. Finally, we use the classifier to recognize the disease. Extracting a meaningful feature from an image is a central problem for a variety of computer vision problems like recognition, image retrieval, and classification. In this research, visual feature descriptor that best describe an image with respect to its visual property is explored. It is specifically focusing on recognizing tasks. The experimental results have proved that the combination of visual descriptors with various classifiers such as SVM and Ensemble Classifier produces high quality outcomes when compared to individual descriptors.

• This study is established to develop a consistent and effective snowy weather detection system that can provide drivers with real-time weather information while driving in regions prone to adverse weather conditions. • This study introduces new data fusion assemblies reduced from the SHRP2 NDS datasets by using the time series data and video records. • The study recommends using the one-minute time segmentation in traffic applications related to weather detection thanks to its high accuracy compared to the one-mile distance segmentations. • Results show that using texture images parameters, collected using a systematic dash-camera and using a feature extraction methodology, can help in detecting snowy weather with an accuracy between 76% to 82%. • The new data fusion between external sensors data and texture image parameters were useful to overcome data privacy and non-accessibility of the CANbus data. Introduction: This study introduces a new analysis protocol for detecting real-time snowy weather conditions on freeways by utilizing trajectory-level data extracted from the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) dataset. The data include parameters reduced from a real-time image feature extraction technique, time series data collected from external sensors, and CANbus data collected by the NDS ego-vehicles. To provide flexibility in winter maintenance, two segmentation types of one-minute and one-mile segments were used to sample snowy trips and their matched clear weather trips. Method: In this study, four non-parametric models were developed using six data assemblies to detect snowy weather on freeways. The data assemblies are arranged based on three data sources, including image database extracted from an in-vehicle video camera, sensors, and CANbus data, to examine the effectiveness of snow detection models for different data types considering real-time availability of data. Results: Overall, the developed models successfully detected snowy weather on freeways with an accuracy ranging between 76% to 89%. Results indicated that high accuracy of estimating snowy weather can be accomplished using the data fusion between external sensors data and texture parameters of images, without accessing to CANbus data. Practical Applications: Practical applications can be driven with respect to the time or distance coordinates, using different data fusion assemblies, and data availability. The study proves the importance of employing vehicles as weather sensors in the Connected Vehicles (CV) applications and Variable Speed Limit (VSL) to improve traffic safety on freeways. [ABSTRACT FROM AUTHOR]

The fuzzy vault structure, which is a biometric pattern safety process in which the biometric traits are represented as an unordered group, is an example of a biometric cryptosystem. A Hybrid Fuzzy Vault-Cuckoo Search algorithm is proposed in this article to provide the best recognition when compared to the existing approach. The module's methods include preprocessing, characteristic elimination, creating characteristic vectors, synthesis, and reformation. The proposed approach's performance is assessed using evaluation metrics. [ABSTRACT FROM AUTHOR]

In the Content-based image retrieval process, the semantic gap is a common challenge and the existing Bag-of-words approach is used to reduce the semantic gap. However, the computation complexity was the common problem that occurred during segmentation and the existing model underwent difficulty during the process of image retrieval. The retrieval performance influenced in exploring various sub-spaces for high dimensional data. The proposed approach performed integration of features such as local optimal oriented pattern, grey level co-occurrence matrix, and Alex net from convolution neural network with the multi-subspace randomization and Collaboration for the retrieval of semantic image. Firstly, the image segments the foreground objects from the background regions using Super Pixel-based Salience Segmentation. From the segmented region, the features present in the objects are extracted. The obtained segmented regions are used for integrating the features and will balance the data dimensions of each of the image pixels. The balanced subspace randomization schemee produces multiple partitions of features that are similar-sized random sub-spaces based on the Manhattan distance. The existing models utilized an ImageNet image repository Interval Type-2 Beta fuzzy near sets Corel datasets that obtained average precision of 78.34%. Similarly, fuzzy C-Means clustering and soft label support vector machine utilized both corel and VOC that obtained average precision of 91.45% and 90.72% better when compared to the proposed method that obtained Average precision of 94.36% and 96.03% for Corel and VOC datasets. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is one of the leading causes of blindness in all age groups. Inadequate blood supply to the retina, retinal vascular exudation, and intraocular hemorrhage cause DR. Despite recent advances in the diagnosis and treatment of DR, this complication remains a challenging task for physicians and patients. Hence, a comprehensive and automated technique for DR screening is necessary, which will give early detection of this disease. The proposed work focuses on 16 class classification method using Support Vector Machine (SVM) that predict abnormalities individually or in combination based on the selected class. Our proposed work comprises Gaussian mixture model (GMM), K-means, Maximum a Posteriori (MAP) algorithm, Principal Component Analysis (PCA), Grey level co-occurrence matrix (GLCM), and SVM for disease diagnosis using DR. The proposed method provides an accuracy of 77.3% on DIARETDB1 dataset. We expect this low computational cost will be helpful in the medicine and diagnosis of DR. [ABSTRACT FROM AUTHOR]

A classification system is used for Benign Tumors (BT) and Malignant Tumors (MT) in the abdominal liver. Computed Tomography (CT) images based on enhanced RGS is proposed. Diagnosis of liver diseases based on observation using liver CT images is essential for surgery and treatment planning. Identifying the progression of cancerous regions and Classification into Benign Tumors and Malignant Tumors are essential for treating liver diseases. The manual process is time-consuming and leads to intra and inter-observer variability. Hence, an automatic method based on enhanced region growing is proposed for the Classification of Liver Tumors (LT). To enhance the Liver Region (LR) from the surrounding tissues, Non-Linear Mapping (NLP) is used. Region Growing Segmentation (RGS) is employed to segment the LR, and Expectation-Maximization (EM) algorithm is used to segment the region of interest. Grey Level Co-occurrence Matrix (GLCM) features are extracted from the tumor region, and Nonlinear Random Support Vector Machine (NRSVM) classification is performed to classify the Benign Tumors and Malignant Tumors. The proposed method is tested on a database of medical images collected from Med all Diagnostic Research Centre and attained an accuracy of 96%. The proposed method is beneficial for better liver tumor diagnosis in an optimized method by the medical expert. [ABSTRACT FROM AUTHOR]

Lung cancer is the most suffering disease which is very difficult to identify in advance and it is not easily cure if the stage of cancer becomes more malignant, the lung cancer is similar like other cancers such as breast cancer, colorectal cancer, brain tumour etc. Now-a-days, there are lot of technologies are developed to predict and treating the diseases, but still have some trouble in detecting the cancer nodule more accurately. Due to increasing in number of patients admitted in clinic, hospitals, etc., doctors cannot able to monitor every patient with high care and they failed to guide their patients with greater attention. Accordingly, the radiologists require a technology named Computer Aided Design (CAD) system for precise recognition and classification of lung nodule where the detected node is cancerous or non-cancerous. In the proposed research, the Chest X-Ray (CXR) images are used as an input image for experimenting the research and image processing techniques has been used to classify the nodule as benign or malignant and executed with greater accuracy in prediction and classification level. In this proposed research work, features were extracted from hasil segmentation image by using Grey Level Co- occurrence Matrix (GLCM) method. The extracted features from image are taken as input data and processed with Artificial Neural Network (ANN) Classifier. The classification and training has been done by Artificial Neural Network with back propagation (ANN-BP) method; therefore, the Artificial Neural Network has competitive and greater in executing the results by comparing with the existing methods of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). Therefore, the performance evaluation of Artificial Neural Network has less training time with better accuracy of 87.5%, sensitivity of 97.75% and specificity of 89.75% by classifying the detected nodule as benign or malignant. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB), as a crucial indicator of forest growth and quality, plays an important role in monitoring the global carbon cycle and forest health. Rapid, accurate, and non-destructive assessment of AGB in rubber plantations is beneficial not only for predicting rubber yield but also for understanding the carbon storage potential in tropical areas. Previous studies have employed spectral information and texture features derived from unmanned aerial vehicle data to estimate the AGB of mangroves. However, few studies systematically assessed the effects of grey level co-occurrence matrix parameters for extracting texture features on AGB estimation in rubber plantations. Whether the combination of spectral information and texture features with suitable grey level co-occurrence matrix parameters selection derived from a low-cost unmanned aerial vehicle system can improve the AGB estimation accuracy remains unclear. To this end, this study evaluated the performance of spectral information and texture features derived from UAV-based high-resolution RGB imagery with different textural parameter settings. Three types of machine learning algorithms (support vector regression; random forest; extreme gradient boosting regressor) and stepwise multiple linear regression were used to compare and analyze their performance for AGB estimation of rubber plantations. The results indicated that appropriate textural parameter selection significantly improved the AGB estimation accuracy when using texture features alone. Among four regression techniques, stepwise multiple linear regression exhibited poor performance, while support vector regression performed the best. The best estimation accuracy (R2 = 0.752, RMSE = 28.72 t/ha) was obtained by support vector regression when using the combination of spectral information and texture features with the textural parameters of the orientation of 135°, displacement of 2 pixels, and moving window size parameter of 7 × 7 pixels. The findings suggested that the AGB estimation accuracy can be further improved by the integration of spectral information and texture features when considering appropriate textural parameters.

In the last years, many types of research have introduced different methods and techniques for a correct and reliable image retrieval system. The goal of this paper is a comparison study between two different methods which are the Grey level co-occurrence matrix and the Hu invariants moments, and this study is done by building up an image retrieval system employing each method separately and comparing between the results. The Euclidian distance measure is used to compute the similarity between the query image and database images. Both systems are evaluated according to the measures that are used in detection, description, and matching fields which are precision, recall, and accuracy, and addition to that mean square error (MSE) and structural similarity index (SSIM) is used. And as it shows from the results the Grey level co-occurrence matrix (GLCM) had outstanding and better results from the Hu invariants moment method.

Agriculture has become the main sources of the income for many developed countries. The productivity in agriculture can be affected by various diseases present in the plant due to climatic conditions. The key step to improve the productivity of crops are to detect the disease at the preliminary stage. Automation becomes the best solution for this because it is more difficult to observe the disorders in plants parts. For that an image of affected plant leaf is acquired and segments the affected portion and to recognize the disease by using image processing and computer vision and machine learning techniques. The extracted features from the segmented portion are descripted using Global and Local Visual descriptors. Finally, we use the classifier to recognize the disease. Extracting a meaningful feature from an image is a central problem for a variety of computer vision problems like recognition, image retrieval, and classification. In this research, visual feature descriptor that best describe an image with respect to its visual property is explored. It is specifically focusing on recognizing tasks. The experimental results have proved that the combination of visual descriptors with various classifiers such as SVM and Ensemble Classifier produces high quality outcomes when compared to individual descriptors. [ABSTRACT FROM AUTHOR]

AIM: To develop an automatic tool on screening diabetic retinopathy (DR) from diabetic patients. METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix method and trained a Bayesian model based on these textures. The receiver operating characteristic (ROC) curve was used to estimate the sensitivity and specificity of the Bayesian model. RESULTS: A total of 1000 eyes fundus images from diabetic patients in which 298 eyes were diagnosed as DR by two ophthalmologists. The Bayesian model was trained using four extracted textures including contrast, entropy, angular second moment and correlation using a training dataset. The Bayesian model achieved a sensitivity of 0.949 and a specificity of 0.928 in the validation dataset. The area under the ROC curve was 0.938, and the 10-fold cross validation method showed that the average accuracy rate is 93.5%. CONCLUSION: Textures extracted by grey level co-occurrence can be useful information for DR diagnosis, and a trained Bayesian model based on these textures can be an effective tool for DR screening among diabetic patients.

The identification of surface texture images from machining surfaces using image processing techniques has been a prominent research area in the recent decades. The aim of this paper is to identify various machined surface texture images using machine learning techniques. Charge coupled device is used to capture images of machined components. Based on captured images, twelve statistical features are extracted and feature vector is formed. Grey Level Co-occurrence Matrix is used to extract statistical features from the machined surface images. Four Machine learning algorithms such as Random Forest, Support Vector Machine, Artificial Neural Network and J48 were utilized to characterize machined surfaces. Training and Ten-fold cross validation process is utilized for identification of machined component images. It is found that Artificial Neural Network and Random forest give 100 % training accuracy and 99% cross validation accuracy. Results obtained demonstrate the efficiency of proposed methodology, which is useful for identifying texture images.

Introduction The artichoke is part of the foods from the vegetable group that provide important nutrients like vitamin A and C, potassium and fiber which used as a food and medicine. In the pharmaceutical sector, dried extracts are used in the preparation of pills and capsules. Dried extracts can be prepared from the dehydration of a concentrated extractive solution from herbal materials (leaves, roots, seeds, etc.), resulting in a dried powder. The spray drying is widely used in the preparation of dried powders from extracts of medicinal plants, fruit pulps. One of the newly developed spray drying techniques is an ultrasonic vacuum method, which strengths of spray drying by incorporation of ultrasonic atomizer and vacuum chamber. Nowadays, image processing has been applied to food images, as acquired by different microscopic systems, to obtain numerical data about the morphology and microstructure of the analyzed foods. For this purpose, microscopy and image processing techniques could be considered as proper tools to evaluate qualitatively and quantitatively the food microstructure, making possible to carry out numerical correlations between microstructure data, as obtained from the images, and the textural properties of food powders. The textural characteristics of the obtained dried powders are determined by means of a perfect detection by scanning electron microscopy (SEM) pictures, and analyzed with a statistical approach for image texture studies, which calls the gray level co-occurrence matrix (GLCM) technique. The object of this study was to illustrate the application of image processing to the study of texture properties from extract powder using GLCM texture analysis and some vacuum spray dryer conditions effect on the texture features of mass particles and single particle SEM images. Materials and Methods After preparing water extract solution from artichoke leaves, extracts were dried under four conditions of vacuum spray drying (according to Table 1). To study the texture of the obtained dried extract powders, different representative features are extracted from the GLCM matrix. The angular second moment (ASM), which is defined as a measure of the homogeneity of the image, the contrast parameter (CT), which represents the amount of local variations given by differences in the gray values in the image. The correlation value (CR), which is a measure of gray tone linear dependencies in the image depending on the direction of the measure (different θs). The inverse difference moment value (IDM), which, similar to ASM, quantifies the homogeneity of the image, however, using a different equation, the entropy parameter (ET), which is a measure that is inversely related to the order given by the gray tones in the image. Rangefilt and stdfilt calculates the local range and local standard deviation of an image respectively. Entropyfilt calculates the local entropy of a grayscale image also. Parameters (ASM, CT, CR and IDM were analyzed in four directions (0º, 45º, 90º, and 135º). Results and Discussion The results of analysis of variance showed that, the difference between the textural features of a single particle and mass particles in four different conditions vacuum spray dryer was significant statistically. Texture analysis was demonstrated that larger ASM, CR, and IDM values indicate less roughness, whereas larger CT and ET values indicate more roughness. At lower inlet temperature and higher vacuum pressure, water diffusion in the material to be slower and allowing the deformation process in the particles to be more pronounced. Consequently, it was possible to observe that generated smaller particles are rougher and less spherical. When the concentration is increased, due to the constant concentration of the additive, the ratio of excipient (lactose) to extraction decreased, as a result were formed a greater number of particles with rougher surfaces. According to these conditions, the values of CT, ET, rangefilt and stdfilt were larger while ASM, CR, and IDM values were smaller. By analyzing the effect of the angle on the oriented textural characteristics, the contrast and correlation parameter were maximum at the angles of 45 and 135 degrees and 0 and 90 degrees respectively. Conclusions Image processing could be auxiliary tools for understanding and characterizing complex systems such as food and biological materials. In this study imaging-based technique was developed to evaluate the texture properties of artichoke leaf extract powder at different conditions of vacuum spray drying. The use of higher temperatures and lower vacuum pressures contributed to faster evaporation rate and production of smoother and larger particles, thereby increasing ASM, CR, and IDM values and reducing CT, ET, Rangefilt and stdfilt. Furthermore, the contrast and entropy parameters showed inverse trends in comparison with correlation, energy and homogeneity. Decrease of solution concentration resulted in the more presence of lactose in the composition of extract/excipient improves the textural properties of powders. The direction parameter had also affected on GLCM textural features. Two oriented textural characteristics (contrast and correlation) also showed significant differences with respect to the nature of particle texture in different directions of measurement. The obtained data extracted from image analysis may provide valuable information to understand the role of structure with respect to product functionality.

The current practice of drug inspection is usually carried out at school or university. This procedure, however, is not effective and efficient, as the urine samples are taken randomly. In many cases, the drug-taking student is not present or evades the urine or hair inspection. A predictive drug user tool is needed, where only suspected student drug users are selected for a urine test. In general, drug abuse constantly causes terrible damage to the skin lesions Since they damage the skin during hallucinations due to the effects of drugs. The Grey Level of Occurrence Matrix (GLCM) is used in this study to discover the scratch pattern. Our proposed GLCM is evaluated with 104 images collected from the Internet. Training data is generated from 88 images of people before and after the drug was collected from the Internet, and we set 16 image faces to test the prediction. The experiment shows that the prediction based on GLCM has better accuracy (81%) compared with the local binary pattern (LBP) which only reach up to 75% [ABSTRACT FROM AUTHOR]

Identifying wood species using wood anatomy is an important tool for various purposes. The traditionally used method is based on the macroscopic description of the physical and anatomical characteristics of the wood. This requires that the identifier has thorough technical knowledge about wood anatomy. A possible alternative to this task is to use intelligent systems capable of identifying species through an analysis of digital images. In this work, 21 species were used to generate a set of 2000 macroscopic images. These were produced with a smartphone under field conditions, from samples manually polished with knives. Texture characteristics obtained through a gray level co-occurrence matrix were used in developing classifiers based on support vector machines. The best model achieved a 97.7% accuracy. Our study concluded that the automated identification of species can be performed in the field in a practical, simple and precise way. [ABSTRACT FROM AUTHOR]

There are numerous conventional fields of natural gas in the Carpathian Foredeep, and there is also evidence to suggest that unconventional gas accumulations may occur in this region. The different seismic signatures of these geological forms, the small scale of amplitude variation, and the large amount of data make the process of geological interpretation extremely time consuming. Moreover, the dispersed nature of information in a large block of seismic data increasingly requires automatic, self-learning cognitive processes. Recent developments with Machine Learning have added new capabilities to seismic interpretation, especially to multi-attribute seismic analysis. Each case requires a proper selection of attributes. In this paper, the Grey Level Co-occurrence Matrix method is presented and its two texture attributes: Energy and Entropy. Haralick's two texture parameters were applied to an advanced interpretation of the interval of Miocene deposits in order to discover the subtle geological features hidden between the seismic traces. As a result, a submarine-slope channel system was delineated leading to the discovery of unknown earlier relationships between gas boreholes and the geological environment. The Miocene deposits filling the Carpathian Foredeep, due to their lithological and facies diversity, provide excellent conditions for testing and implementing Machine Learning techniques. The presented texture attributes are the desired input components for self-learning systems for seismic facies classification. [ABSTRACT FROM AUTHOR]

The present study proposes a workflow to extract from orthomosaics the enormous amount of dry stones used by past societies to construct funeral complexes in the Mongolian steppes. Several different machine learning algorithms for binary pixel classification (i.e. stone vs non-stone) were evaluated. Input features were extracted from high-resolution orthomosaics and digital elevation models (both derived from aerial imaging). Comparative analysis used two colour spaces (RGB and HSV), texture features (contrast, homogeneity and entropy raster maps), and the topographic position index, combined with nine supervised learning algorithms (nearest centroid, naive Bayes, k -nearest neighbours, logistic regression, linear and quadratic discriminant analyses, support vector machine, random forest, and artificial neural network). When features are processed together, excellent output maps, very close to or outperforming current standards in archaeology, are observed for almost all classifiers. The size of the training set can be drastically reduced (to ca. 300 samples) by majority voting, while maintaining performance at the highest level (about 99.5% for all performance scores). Note, however, that if the training set is inadequate or not fully representative, the classification results are poor. That said, the methods applied and tested here are extremely rapid. Extensive mapping, which would have been difficult with traditional, manual, or semi-automatic delineation of stones using a vector graphics editor, now becomes possible. This workflow generally surpasses pedestrian surveys using differential GPS or a total station. [ABSTRACT FROM AUTHOR]

Texture analysis is key to better understanding of the relationships between the microstructures of the materials and their properties, as well as the use of models in process systems using raw signals or images as input. Recently, new methods based on transfer learning with deep neural networks have become established as highly competitive approaches to classical texture analysis. In this study, three traditional approaches, based on the use of grey level co-occurrence matrices, local binary patterns and textons are compared with five transfer learning approaches, based on the use of AlexNet, VGG19, ResNet50, GoogLeNet and MobileNetV2. This is done based on two simulated and one real-world case study. In the simulated case studies, material microstructures were simulated with Voronoi graphic representations and in the real-world case study, the appearance of ultrahigh carbon steel is cast as a textural pattern recognition pattern. The ability of random forest models, as well as the convolutional neural networks themselves, to discriminate between different textures with the image features as input was used as the basis for comparison. The texton algorithm performed better than the LBP and GLCM algorithms and similar to the deep learning approaches when these were used directly, without any retraining. Partial or full retraining of the convolutional neural networks yielded considerably better results, with GoogLeNet and MobileNetV2 yielding the best results.

This paper is focusing on feature extraction methods for malignant masses in mammograms and its classification. It proposes seven texture features for GLCM method and to be applied on sub-images to enhance its performance. It also proposes three hybrid methods named Wavelet-CT1, Wavelet-CT2 and ST-GLCM. The three hybrid methods are merging two types of different features. In this research, we divide the region of interest image into s × s sub-images and a contrast stretching stage is applied before extracting the features from each sub-image. This research also introduces two Contourlet methods (CT1 and CT2). The feature extraction methods are applied on each sub-image of ROI. CT1 is applying Contourlet at level 4. CT2 is applying Contourlet at levels [4321]. GLCM uses seven texture features. Wavelet-CT1 is applying CT1 method to all bands of wavelet coefficients at level one. Wavelet-CT2 is merging high frequency bands of wavelet at level one with contourlet coefficients of CT2. ST-GLCM merges seven statistical features and seven texture features extracted from Grey level Co-occurrence Matrix (GLCM). The proposed methods are compared with multi-resolution feature extraction methods using discrete wavelet, ridgelet and curvelet transform. SVM is used for classification. Images from Digital Database for Screening Mammography (DDSM) and Mammograms Image Analysis Society (MIAS) database are used for evaluation. The performance of proposed methods ST-GLCM, GLCM, Wavelet-CT1 and Contourlet (CT2) outperform all current existing feature extraction methods in terms of AUC measure. The extracted number of features by using GLCM or ST-GLCM is small compared to multi-resolution features.

Abstract Background Diagnosis of pulmonary thromboembolism (PTE) in dogs relies on computed tomography pulmonary angiography (CTPA), but detailed interpretation of CTPA images is demanding for the radiologist and only large vessels may be evaluated. New approaches for better detection of smaller thrombi include dual energy computed tomography (DECT) as well as computer assisted diagnosis (CAD) techniques. The purpose of this study was to investigate the performance of quantitative texture analysis for detecting dogs with PTE using grey-level co-occurrence matrices (GLCM) and multivariate statistical classification analyses. CT images from healthy (n = 6) and diseased (n = 29) dogs with and without PTE confirmed on CTPA were segmented so that only tissue with CT numbers between −1024 and −250 Houndsfield Units (HU) was preserved. GLCM analysis and subsequent multivariate classification analyses were performed on texture parameters extracted from these images. Results Leave-one-dog-out cross validation and receiver operator characteristic (ROC) showed that the models generated from the texture analysis were able to predict healthy dogs with optimal levels of performance. Partial Least Square Discriminant Analysis (PLS-DA) obtained a sensitivity of 94% and a specificity of 96%, while Support Vector Machines (SVM) yielded a sensitivity of 99% and a specificity of 100%. The models, however, performed worse in classifying the type of disease in the diseased dog group: In diseased dogs with PTE sensitivities were 30% (PLS-DA) and 38% (SVM), and specificities were 80% (PLS-DA) and 89% (SVM). In diseased dogs without PTE the sensitivities of the models were 59% (PLS-DA) and 79% (SVM) and specificities were 79% (PLS-DA) and 82% (SVM). Conclusion The results indicate that texture analysis of CTPA images using GLCM is an effective tool for distinguishing healthy from abnormal lung. Furthermore the texture of pulmonary parenchyma in dogs with PTE is altered, when compared to the texture of pulmonary parenchyma of healthy dogs. The models’ poorer performance in classifying dogs within the diseased group, may be related to the low number of dogs compared to texture variables, a lack of balanced number of dogs within each group or a real lack of difference in the texture features among the diseased dogs.

Obtaining water body images quickly and reliably is important to guide human production activities and study urban change. This paper presents a fast and accurate method to identify water bodies in complex environments based on UAV high-resolution images. First, an improved U-Net (SU-Net) model is proposed in this paper. By increasing the number of connections in the middle layer of the neural network, more image features can be retained through S-shaped circular connections. Second, aiming at the interference of mixed ground objects and dark ground objects on water detection, the fusion of a deep learning network and visual features is investigated. We analyse the influence of a wavelet transform and grey level cooccurrence matrix (GLCM) on water extraction. Using a confusion matrix to evaluate accuracy, the following conclusions are drawn: (1) Compared with existing methods, the SU-Net method achieves a significant improvement in accuracy, and the overall accuracy (OA) is 96.25%. The kappa coefficient (KC) is 0.952. (2) SU-Net combined with the GLCM has a higher accuracy (OA is 97.4%) and robustness in distinguishing mixed and dark objects. Based on this method, a distinct water boundary in urban areas, which provides data for urban water vector mapping, can be obtained.

Today, over half of the world's population lives in urban areas and it is projected that, by 2050, two out of three people will live in a city. This increased rural–urban migration, coupled with housing poverty, has led to the growth and formation of informal settlements, commonly known as slums. In Mexico, 25% of the urban population now live in informal settlements with varying degrees of deprivation. Although some informal neighbourhoods have contributed to the upward mobility of the inhabitants, the majority still lack basic services. Mexico City and the conurbation around it form a mega city of 21million people that has been growing in a manner qualified as 'highly unproductive, (that) deepens inequality, raises pollution levels' (available at: https://www.smartcitiesdive.com/ex/sustainablecitiescollective/making-way-urban-reform-mexico/176466/) and contains the largest slum in the world: Neza-Chalco-Izta. Urban reforms are now aiming to improve the conditions in these slums and therefore it is very important to have reliable tools to measure the changes that are underway. In this paper, we use exploratory factor analysis to define an index of shelter deprivation in Mexico City, namely the Slum Severity Index (SSI), based on the UN-HABITAT's definition of slum. We apply this novel approach to the Census survey of Mexico and measure the shelter deprivation levels of households from 1990 to 2010. The analysis highlights high variability in housing conditions within Mexico City. We find that the SSI decreased significantly between 1990 and 2000 as a result of several policy reforms but increased between 2000 and 2010. We also show correlations of the SSI with other social factors such as education, health and fertility. We present a validation of the SSI using Grey Level Co-occurrence Matrix (GLCM) features extracted from Very-High Resolution (VHR) remote-sensed satellite images. Finally, we show that the SSI can present a cardinally meaningful assessment of the extent of deprivation compared with a similar index defined by Connolly (Connolly P (2009) Observing the evolution of irregular settlements: Mexico city's colonias populares, 1990 to 2005. International Development Planning Review 31: 1–35) that studies shelter deprivation in Mexico. [ABSTRACT FROM AUTHOR]