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This article takes the complex uncertainty in the carrying process of water resources system as the research object. Firstly, the water resources carrying capacity system is divided into three sub-systems: bearing support capacity, bearing pressure and bearing regulation capacity. System analysis methods based on wireless sensor network and image texture of feature extraction for correlation analysis, gray correlation analysis, and multi-dimensional conditional cloud algorithm are introduced into the field of water resources carrying capacity evaluation research. Moreover, combine it with the subtraction set opposition, the effect partial bias connection number, and the risk matrix. Finally, three types of models for water resource carrying capacity evaluation and trend analysis were constructed. The evaluation results of water resources carrying capacity are consistent with the actual process, and the overall development trend is gradually improving. The application results of the evaluation model of water resource carrying capacity are established. The calculation results of the image texture feature of extraction model are similar to the overall change trend of the model before the improvement. The trend of change is more obvious. It can not only describe the water resource carrying level as a whole, but also characterize the reliability of the calculation results and the bearing risk caused by the uncertainty of the water resource carrying system. The water resources carrying capacity evaluation and trend analysis of calculation results are reasonable and feasible. The research results can provide a reasonable and effective decision-making basis for the systematic analysis of the bearing boundary issues between regional complex water resources systems and human water activities, and then propose adaptive risk prevention and control strategies for water resources carrying capacity.

The performance of machine learning (ML)-based path loss models relies heavily on the data they use at their inputs. Feature engineering is, therefore, essential for the model's success. In this letter, at hand, we extract a new set of input features, based on image texture. The image that we use represents the footprint of the urban built-up area, where the gray scale values of the building blocks are analogue to their heights. We extract texture information by applying the segmentation-based fractal texture analysis algorithm on the orthogonal area that is bounded between the transmitter and the receiver. To the best of our knowledge this is the first time that such a technique is applied to a path loss modeling problem in electromagnetics. The algorithm, thus, delivers a new set of features, based on the image's texture, which eventually reveal the built-up profile of the area. These new features are injected to an already existing feature set. Comparative analysis shows that the addition of texture-based features leads to enhanced predictions, for a diverse set of transmitter heights, ML algorithms, and performance metrics.

Recognition of spalling on surface of concrete wall is crucial in building condition survey. Early detection of this form of defect can help to develop cost-effective rehabilitation methods for maintenance agencies. This study develops a method for automatic detection of spalled areas. The proposed approach includes image texture computation for image feature extraction and a piecewise linear stochastic gradient descent logistic regression (PL-SGDLR) used for pattern recognition. Image texture obtained from statistical properties of color channels, gray-level cooccurrence matrix, and gray-level run lengths is used as features to characterize surface condition of concrete wall. Based on these extracted features, PL-SGDLR is employed to categorize image samples into two classes of “nonspall” (negative class) and “spall” (positive class). Notably, PL-SGDLR is an extension of the standard logistic regression within which a linear decision surface is replaced by a piecewise linear one. This improvement can enhance the capability of logistic regression in dealing with spall detection as a complex pattern classification problem. Experiments with 1240 collected image samples show that PL-SGDLR can help to deliver a good detection accuracy (classification accuracy rate = 90.24%). To ease the model implementation, the PL-SGDLR program has been developed and compiled in MATLAB and Visual C# .NET. Thus, the proposed PL-SGDLR can be an effective tool for maintenance agencies during periodic survey of buildings.

Fractal image compression coding algorithm is a novel image compression technology; however, the long encoding time and unacceptable image reconstruction quality remain the primary obstacles in practical application. The purpose of this study is to improve the coding quality from the perspective of grey level transform and feature extraction. In this study, a novel orthogonal sparse fractal coding algorithm based on image texture feature is proposed. The authors define a normalised version as the new grey description feature of the image block so that two improved methods are scientifically combined in theory and algorithm. First, orthogonal sparse grey level transform based on sparse decomposition improves image reconstruction quality and decoding speed. Then, the similarity measure matrix, which stores the variance feature between range blocks and domain blocks, is used to reduce redundancies and encoding time. Simulation results show that the proposed algorithm in this study can obtain better image reconstruction quality and speed up encoding time significantly as compared to the conventional fractal coding schemes.

The traditional image texture feature recognition method is deeply affected by the complexity of the image background and the amount of noise, resulting in a low recognition rate. To solve the above problems, an image texture feature recognition method based on rotation invariant histogram is proposed. After denoising the image to be recognized, the image texture feature is extracted and the image feature texture set is established. After the rotation invariant region of the image histogram is selected, the classifier is used to recognize the image texture features. Design a comparative experiment with the current recognition method. The experimental results show that the research method can improve the recognition accuracy by about 12.9%, and the recognition time is short and reliable.

Tongue image texture analysis is the main content of inspection diagnostics in traditional Chinese medicine (TCM). The tough and tender classification for tongue image is mainly represented by image texture. The traditional extraction method of texture feature is not robust and is not suitable for variable illumination and different image acquisition equipment. So we select 'Xception' as the main network model to complete feature extraction firstly. Then, two full connection layers and a softmax layer are combined, two dropout layers are added in their middle to form a classification network. The experimental results show that the better accuracy rate can be obtained by our method.

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

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., 12 pages, 5 figures. Accepted and to be published in Pattern Recognition Letters

In this paper, a scheme of facial texture synthesis and 3D reconstruction based on 2D face image and 3D model under the Unity platform is proposed. First of all, this scheme uses the ASM algorithm to extract the key points of the human face. Next, the facial features in the face image and the standard skin color image are texture blended to get the texture of fused facial image. Finally, the texture of fused facial image is mapped to the 3D face model by texture mapping, and a better personalized 3D face reconstruction effect is obtained by this method. Compared with FaceGen, a software for modeling facial photos, the experimental results show that the proposed algorithm is superior to other existing algorithms in efficiency and has certain application value.

Based on image processing, feature extraction was a key part of against too much when Gabor filter to extract the texture feature parameters, difficult to find the appropriate parameters of the problem, after the analysis of mechanism of Gabor filter and various parameters, considering the characteristics of the intelligent algorithm is good at finding the optimal solution, designed by genetic algorithm selection, crossover and mutation operation, find out the objective function of the fitness of Gabor parameters, so as to realize the structure of the Gabor filter through the simulation, the scheme of the correctness of the Gabor filter.

Background Accurately determining the softness level of pituitary tumors preoperatively by using their image textures can provide a basis for surgical options and prognosis. Existing methods for this problem require manual intervention, which could hinder the efficiency and accuracy considerably. Methods We present an automatic method for diagnosing the texture of pituitary tumors using unbalanced sequence image data. Firstly, for the small sample problem in our pituitary tumor MRI image dataset where T1 and T2 sequence data are unbalanced (due to data missing) and under-sampled, our method uses a CycleGAN (Cycle-Consistent Adversarial Networks) model for domain conversion to obtain fully sampled MRI spatial sequence. Then, it uses a DenseNet (Densely Connected Convolutional Networks)-ResNet(Deep Residual Networks) based Autoencoder framework to optimize the feature extraction process for pituitary tumor image data. Finally, to take advantage of sequence data, it uses a CRNN (Convolutional Recurrent Neural Network) model to classify pituitary tumors based on their predicted softness levels. Results Experiments show that our method is the best in terms of efficiency and accuracy (91.78%) compared to other methods. Conclusions We propose a semi-supervised method for grading pituitary tumor texture. This method can accurately determine the softness level of pituitary tumors, which provides convenience for surgical selection and prognosis, and improves the diagnostic efficiency of pituitary tumors.

Ice storms can cause major power disruptions and detection of ice formation on power cables can avoid them by taking preventive actions such as removing the ice before a major problem occurs. In this paper, a computer vision solution was developed to detect ice on difficult imaging scenarios where illumination and variety of locations can change the image of the cable and background considerably. The methodology starts with edge detection and support vector regression was applied to predict the upper threshold for the Canny edge detector. We found that this improves the later classification accuracy. A total of 44 image features based on the gray level co-occurrence matrix, statistical features and Hough transform were extracted and then processed by principal component analysis and sequential forward selection to reduce the feature dimension. Final detection was performed using four different classifiers. It was found that eight features achieved optimal performance using a support vector machine.

Induction motors (IM) are the most commonly used machines in industry; hence, early diagnosis of IM faults has become a subject of great interest for many researches, since if there is a fault and the IM keep working for long time, they and their associated systems might suffer catastrophic damages. Broken Rotor Bars (BRB) are among the most common and difficult to detect faults. Many methodologies based on Motor Current Signature Analysis (MCSA) for BRB detection have been proposed in recent years, which usually require long execution time and specialized hardware and software for their implementation. Therefore, in this work, a novel and straightforward methodology for BRB detection and classification during the induction motor steady state, based on MCSA, image texture feature (contrast), and a fuzzy logic classifier is proposed. Obtained results demonstrate the suitability of the proposed approach for identifying BRB with high effectiveness.

Under long-term synergy effect of multi-factors, especially the thermal stress, insulating paper will be degraded and its insulation performance will decline due to carbonization and degradation of cellulose. This paper presents an optical approach for aging feature extraction of the insulating paper, where one of the image processing methods called texture analysis is utilized. By conducting laboratory accelerated thermal aging tests, insulating paper samples with different aging conditions for both Nomex and Kraft, evaluated with the aging time, are prepared. After taking optical microscopic images of insulating paper samples belong to different aging groups, up to 14 texture features are extracted using the gray-level co-occurrence matrix (GLCM). With different feature selection methods applied, several of them are finally selected to represent the aging condition of insulating. Numerical tests with both supervised and unsupervised algorithms, as well as a linear regression method verifies the validity of these features in characterizing the aging condition of the insulating paper.

One of the most important factors for deciding the use of wood product is knowing its strength class. So far, the wood strength has been measured using relatively expensive laboratory tests both for purchase and rent. In addition, this test has also damaged the test wood. An alternative method that can be used to determine the strength class of woods is to observe their characteristics using the naked eye. However, this is also a tough challenge because it must be done by wood experts who truly memorize and understand the characteristics of each wood class. Therefore, an image processing method is needed to identify the strength of wood easily, cheaply and not to damage test wood.The object of this study consisted of seven types of wood: Acacia (Acacia mangium), Teak (Tectona grandis), Mahogany (Swietenia mahagoni), Silk Tree (Paraserianthes falcataria), Sengon Tekik, Rain Tree (Samanea Saman) and Coastal Cottonwood (Hibiscus tiliaceus). Based on the results of specific gravity measurement, the wood strength classes of the test wood were as follows : Acacia, Teak and Mahogany were Class II, Rain Tree and Coastal Cottonwood were Class III, while Silk Tree and Sengon Tekik were Class IV.The parameters used by the author to determine the strength of wood based on image processing are the RGB color and texture of the wood image. We use two feature extraction methods, the average RGB histogram value for each color channel and the static characteristics of the Gray-Level Co-Occurrence Matrix (GLCM) method. The method used for feature selection is Recursive Feature Elimination Cross Validation (RFECV). While for data classification we use five algorithms: K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Decision Tree (CART), Random Forest (RF), and Support Vector Machine (SVM). The results of the qualifications showed that the highest accuracy was obtained by the KNN of 95%. Therefore, this method can be used as an alternative to determine the strength of wood.

A sign language is a mode of communication in which the intent of the message or the message itself is conveyed through body postures or the movement of the parts of the body like head, eyebrows and cheeks etc. Every expression is distinct and has distinguishable parts of language, its grammatical content fully displayed through gestures. There are more than three hundred sign languages used in the world. The baby sign language is a method of communication between the mothers and their toddlers by means of gestures, clearly expressing their emotions and desires. In the present research work study of the existing literature has been carried out and then prepared a data set of still jpeg images for 60 odd baby signs, performed GLCM(Gray Level Co-Occurrence Matrix) based feature extraction, then performed classification of gestures using KNN and Random Forest based machine learning algorithms. A classification accuracy of 73% has been achieved on the dataset prepared.

Background: Feature extraction is an essential part of a Computer-Aided Diagnosis (CAD) system. It is usually preceded by a pre-processing step and followed by image classification. Usually, a large number of features is needed to end up with the desired classification results. In this work, we propose a novel approach for texture feature extraction. This method was tested on larynx Contact Endoscopy (CE)—Narrow Band Imaging (NBI) image classification to provide more objective information for otolaryngologists regarding the stage of the laryngeal cancer. Methods: The main idea of the proposed methods is to represent an image as a hilly surface, where different paths can be identified between a starting and an ending point. Each of these paths can be thought of as a Tour de France stage profile where a cyclist needs to perform a specific effort to arrive at the finish line. Several paths can be generated in an image where different cyclists produce an average cyclist effort representing important textural characteristics of the image. Energy and power as two Cyclist Effort Features (CyEfF) were extracted using this concept. The performance of the proposed features was evaluated for the classification of 2701 CE-NBI images into benign and malignant lesions using four supervised classifiers and subsequently compared with the performance of 24 Geometrical Features (GF) and 13 Entropy Features (EF). Results: The CyEfF features showed maximum classification accuracy of 0.882 and improved the GF classification accuracy by 3 to 12 percent. Moreover, CyEfF features were ranked as the top 10 features along with some features from GF set in two feature ranking methods. Conclusion: The results prove that CyEfF with only two features can describe the textural characterization of CE-NBI images and can be part of the CAD system in combination with GF for laryngeal cancer diagnosis.

In this paper, we utilize time-frequency image representations of sound signals for feature extraction in an audio surveillance application. Starting with the conventional spectrogram images, we consider a new feature which is based on image texture analysis. It utilizes the gray-level co-occurrence matrix, which captures the distribution of co-occurring values at a given offset. We refer this as the spectrogram image texture feature. Texture analysis is carried out in subbands and experimented on a sound database containing ten classes with each sound class containing multiple subclasses. The proposed feature was seen to be more noise robust than two commonly used cepstral features, mel-frequency cepstral coefficients and gammatone cepstral coefficients, the spectrogram image feature (SIF), where central moments are extracted as features, and a variation of SIF with reduced feature dimension. In addition, we achieved a significant improvement in classification accuracy for the three time-frequency image features by utilizing a gammatone filter-based time-frequency image, referred as cochleagram image, for feature extraction instead of the spectrogram image. A combination of cepstral and cochleagram image features also gave improvement in the classification performance.

Deep learning has made great progress in the field of face recognition, but most of the current face feature matching algorithms focus on the matching of a single image and another single image, and can not effectively use the relevant information between image sequences, in order to avoid the influence of human factors on the skin texture feature extraction of face image. In this paper, a texture spectrum feature extraction method based on deep learning is proposed. The face image is extracted by CNN network, and the similar image sequences are automatically selected for feature matching by using the improved sparse expression method to obtain the relevant information between the face image sequences. The experimental results show that the algorithm has achieved good results in LFW and AR databases and is superior to the traditional SRC, L1 norm and CRC-RLS algorithms.

Knee Osteoarthritis (OA) is one of the most prominent diseases in an ageing society and has affected over 10 million people in Thailand. When people suffer from OA, it is very difficult to recover. Therefore, early detection and prevention are important. The typical way to detect OA is by using X-ray imaging. This research study is focused on early detection of OA by applying image processing and classification techniques to knee X-ray imagery. The fundamental concept is to find a region of interest, use feature extraction techniques and build a classifier that can classify between OA or non-OA imageries. There are four regions of interest obtained from each image: (i) Medial Femur (MF), (ii) Lateral Femur (LF), (iii) Medial Tibia (MT), and (iv) Lateral Tibia (LT). The ten texture analysis techniques are then adopted to generate the embedded properties of the bone surface. Once the feature vector has been generated the variety of techniques of machine learning mechanisms are applied to generate the desired classifiers, which can be used to distinguish between OA and non-OA images. From the conducted experiments, a total of 131 images (68 OA cases and 63 non-OA cases) was used, the results obtained show that LF region with Local Binary Pattern descriptor produced the most appropriate classifier with an AUC value of 0.912.

This article proposes an algorithm for script identification by textural analysis of the image corresponding to the script types. In the first phase, each letter is modeled by the equivalent script type, which is determined by its position in the baseline area. Then, feature extraction is carried out. It is based on the script type cooccurrence pattern analysis. The obtained features of the script are stored for further analysis. The difference in script characteristics contributes to the diversity of the extracted features, which simplify the feature classification obtained by an extension of a state-of-the-art classification tool called Genetic Algorithms Image Clustering for Document Analysis. Accordingly, it represents the key element in the decision-making process of script identification. The proposed method is tested on an example of German printed documents, which contain Latin and Fraktur scripts. The experiment shows correct results, which is promising.

Matching in high-resolution synthetic aperture radar (SAR) images while being more complicated compared to optical images, is especially important due to its numerous applications. The main aim of current research is to determine improvement of SAR image matching process by deploying texture analysis using gray level's co-occurrence matrix (GLCM). Three parts of the pair of TerraSAR-X images are used to implement the methodology. The results show that for some areas with low texture, the conventional image matching algorithm is not able to detect corresponding points, while using other textural features in image matching process leads to improvement in quantity of acceptable matched points.

Efforts are afoot to design better context-aware human-computer interaction techniques that have knowledge of both their surrounding and the affective state of the user. One of the most important nonverbal behavioural cues for affective human-machine interaction is laughter. Automatic detection of laughter is an interesting, yet challenging problem, which in recent years has gained increased attention from both the academic and industrial communities. The majority of existing laughter detection systems rely on either audio or video modalities. Humans, however, typically rely on audio-visual cues during conversation and/or interaction, thus it is expected that improved results can be achieved if both modalities are used. In this work, we propose a multimodal framework that analyzes audio and video channels separately, then fuses their decisions. Conventional speech spectral and prosodic features are used, whereas new multi -scale multiresolution binarized statistical image features are proposed due to their improved expressive power. Experiments with the publicly available MAHNOB Laughter database show that decision level fusion based on support vector machine classifiers leads to improved performance over single modality approaches, as well as over previously-proposed methods, all whilst requiring just a fraction of the computational power.

In a complicated cloud storage environment in which users upload a large number of files everyday, in order to better solve the challenge of inefficient malicious detection and weak adaptability of multi-platform detection in the traditional way, we propose a malicious file detection method which is based on image texture analysis and BP neural network algorithm. By combining the technology of image analysis and the malicious file detection, the malicious file is converted into grayscale image, the GLCM (Ground Launched Cruise Missile) and the GIST (Generalized Search Trees) algorithms are used to extract the texture features, and the BP neural network algorithm is then used for learning and training. In this paper, we propose and implement a malicious file detection system by means of image texture extraction. Through the experimental analysis on a large number of virus samples from the well-known VirusShare project, the experimental results show that our proposed approach has the characteristics of fast speed, high adaptability and high accuracy.

MaZda is a software package that provides a complete path for quantitative analysis of image texture and color, including image recognition, detection, and segmentation. The texture feature computation algorithms are generalized to three dimensions, to allow for analysis of 3D data from magnetic resonance imaging or computed tomography scanners. Moreover, the software can be used for computation of morphological features (shape properties) in segmented images. MaZda was utilized by numerous researchers in diverse applications. It has proven to be efficient and reliable for quantitative image analysis. It was successfully used in medical image analysis and food product quality assessment. This chapter presents the origins of MaZda, discusses the algorithms implemented in the software, shows selected applications, and recommends image analysis procedures. Finally, MaZda is compared to other software tools for image texture and color characterization and to image recognition. It is free and open-source software.

A statistical classification method using Mel cepstrum and image texture is introduced to passive sonar target recognition. Image texture features are extracted by regarding Mel frequency cepstrum coefficients (MFCC) as a digital image. Bayesian formula and Markov chain are applied to the recognition algorithm. The classification experiments are carried out for three different kinds of targets based on practical data. The experimental results show that the proposed method is useful.

The widespread penetration of counterfeit integrated circuits (ICs) is not only a major threat to the electronic goods supply chain, but also constitute a great threat to national security. Image processing based counterfeit IC design techniques are promising, but currently often suffer from high computational complexity and requirement of expensive image acquisition infrastructure. We describe two techniques based on image texture analysis to automate the process of counterfeit IC detection. The first method employs local textural feature identification to detect counterfeit ICs. The second method includes identification of counterfeit ICs by segmenting the image into regions of different textural features using texture filters. The first method is of lower computational complexity compared to the segmentation method, but the second method is capable of blind identification in the sense that it does not require knowledge of the textural features of a golden IC sample. Our experimental results show that these methods have high detection accuracy, even for images acquired using ordinary digital cameras and low-end digital microscopes.

Specific emitter identification (SEI) aims at discerning radio emitters using external measurement features called the radio frequency (RF) fingerprints of the signal. SEI is widely used in intelligent gathering, network intrusion detection, Cognitive Radio and so on. In this paper, we proposed a novel SEI method based on steady signals, which is more practical. We firstly get the time-frequency-energy distribution (TFED) of the signal using Intrinsic Time-scale Decomposition(ITD), which is more suitable for non-stationary signal than empirical mode decomposition (EMD). Secondly, we transform the TFED of the signal into a gray image and several image texture features are extracted from histogram statistics and Gray-Level Co-Occurrence Matrix (GLCM) of the image. We treat these texture features as the signal's RF fingerprints to identify the specific emitter. Measured ship signals and simulated signals are used to evaluate the performance of the proposed method and another two methods are compared with it. Experimental results demonstrate that the proposed method is more effective.

This paper introduces an image texture analysis method for minority language identification. In the first stage, each letter is associated with a given script type according to its energy status in the text-line area. Mapping is carried out by extracting unicode text and transforming it into coded text. There are four different script types, which correspond to four grey levels of an image. Then, the obtained image is subjected to a feature extraction process performed by the texture analysis. This way, the grey level co-occurrence matrix and its derivative features are calculated. Extracted features are compared and classified using the K-Nearest Neighbors and Naive Bayes methods to establish a difference that can identify a minority language such as Serbian language among other world languages in the text. Very good accuracy results prove the efficiency of the proposed approach, when compared to other state-of-the-art methods.

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.

We present an algorithm for risk prediction of road surface grip where skidding and sliding occur as main road surface problems. Prediction is done by defining a fine texture classification of the properties of road aggregate. In an experimental setup, data acquisition is performed with a supervised mobile vehicle scanning system, using a vehicle equipped with a camera and temperature sensor during movement along an arterial road. Image processing is performed by testing four texture feature extraction methods: Gabor filters, wavelet transform, gray level co-occurence matrix, and edge histogram descriptor, among which the Gabor transform shows the best results. The extraction of texture feature vectors follows by statistical algorithms for measuring feature vector similarity and reference vector selection, leading to image texture classification. The algorithm itself is upgraded by incorporating simultaneous surface temperature measurements in order to create and validate the final fine surface texture classification. The roads are classified and segmented into high-, medium-, and low-risk roads according to skid danger, enabling the creation of a map of high-risk zones. We validate our risk prediction algorithm by referring to crash rate data from the Road Traffic Safety Agency of Serbia database. This algorithm enables the location and mapping of high-risk zones and can be used as a support for autonomous driving and navigation.

Public transport vehicle scheduling system plays a very important role in relieving urban traffic pressure, and it's now still a difficult problem. In order to solve the problem effectively, we propose a bus scheduling method based on video processing algorithm in this paper. Firstly, we decode the H264 bare stream which receive from the bus in real-time, and according to the prior knowledge, a certain number of training samples with different density levels were collected, and then extract the improved gray-level co-occurrence matrix and color entropy features of training samples to train the classifier model. Finally, crowd density estimation result can get by processing the real time vehicle video using the trained model. Combined the result of density estimation with vehicle status information, we can realize the vehicle scheduling effectively.

Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.

In order to effectively extract Chinese herbal medicine (CHM) image feature information, and automatically identify the CHM images, a method of CHM image feature extraction and recognition based on gray level co-occurrence matrix (GLCM) is put forward. Firstly, on the basis of the acquired colour CHM image is converted to the gray-scale image, the four texture feature parameters, angular second moment (ASM)，inertia moment (IM)，entropy and correlation are extracted utilizing the GLCM, and then CHM image recognition is carried out by using those feature values with resistance geometric distortion. The experimental results show that the method of generating GLCM and extraction of image texture features can effectively identify the CHM image, which can bring significance to the modern recognition and identification of CHM.

International audience; Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity

To determine the real-time traffic state accurately in road network or intersections, traffic state identification method is proposed based on image processing technology. During the image process, by analyzing the image texture features, the multi-scale block local binary patterns are taken as the features. The road traffic state identification model is established based on support vector classification. A road section is taken as an example, the images of three traffic states (free traffic flow, steady traffic flow, and forced traffic flow) are selected to test the established traffic state identification model. This method can be applied to determine the real-time traffic conditions, which can provide information for the traffic management decisions.

In the present paper, a methodological scheme, bringing together common Acoustic Seabed Classification (ASC) systems and a powerful data decomposition approach, called Independent Component Analysis (ICA), is demonstrated regarding its suitability for detecting small targets in Side Scan Sonar imagery. Traditional ASC systems extract numerous texture descriptors, leading to a large feature vector, the dimensionality of which is reduced by means of data decomposition techniques, usually Principal Component Analysis (PCA), prior to classification. However, in the target detection issue, data decomposition should point towards finding components that represent sub-ordinary image information (i.e., small targets) rather than a dominant one. ICA has long been proved to be suitable for separating targets from a background, and this study represents a novel exhibition of its applicability to Side Scan Sonar (SSS) images. The present study attempts to build a fully automated target detection approach that combines image based feature extraction, ICA, and unsupervised classification. The suitability of the proposed approach has been demonstrated using an SSS data-set containing more than 70 manmade targets, most of them metallic, validated through a marine magnetic survey or ground truthing inspection. The method exhibited very good performance as it was able to detect more than 77% of the targets and it produced less than seven false alarms per km2. Moreover, it was compared to cases where, in the exact same methodological scheme, no decomposition technique is used, or PCA is employed instead of ICA, achieving the highest detection rate, but, more importantly, producing more than six times less false alarms, thus proving that ICA successfully manages to maximize target to background separation.

Pattern is an arrangement of features which are defined by various characteristics of image such as shape, color and texture. Texture is an important characteristic for image analysis. The major trend of the research today in terms of feature extraction for classification is accuracy oriented, however usually the newer algorithms that promises better accuracy is much more complicated in its calculations and often sacrifices the speed of the algorithm. This paper contains study and review of various techniques used for feature extraction and texture classification. The objective of study is to find technique or combination of techniques to reduce complexity, speed while increasing the accuracy at the same time. Here we are studying and reviewing the three feature extraction methods: Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter method. Also two classification methods KNN and SVM are used on the texture datasets Brodatz, CUReT, VisTex and OuTex for the experimental purpose.

This paper presents a machine vision system for the automatic characterization of the quality properties of nanofiber assemblies using image texture analysis techniques. The objective is to use a digital image of a nanofiber membrane in order to estimate the pore size distribution (PSD), fiber diameter distribution (FDD) and permeability of the assembly. The underlying idea is that, after textural features have been extracted from the image in the form of statistical descriptors, these descriptors can be related to the membrane properties using multivariate regression techniques. Two alternative feature extraction techniques are considered, a statistical-based approach (namely, gray-level co-occurrence matrix, GLCM) and a transform-based approach (based on the use of wavelet transforms). Polymer nanofiber membranes fabricated by electrospinning are used as test beds of the proposed system. The estimation results are very satisfactory for all properties with any of the two feature extraction techniques, with the wavelet transform-based approach slightly outperforming the GLCM one. Whereas for the estimation of the PSD and FDD a scanning electron microscope image of the product needs to be available, it is shown that as far as permeability is concerned an image at a much lower magnification scale (i.e. an optical microscope one) is sufficient to provide an accurate property estimation. Based on these results, the proposed system represents a very promising step toward the complete automation of quality assessment procedures in nanomaterials processing.

A sound signal produces a unique texture which can be visualized using a spectrogram image and analyzed for automatic sound recognition. In this paper, we explore the use of a well-known image texture analysis technique called the gray-level co-occurrence matrix (GLCM) for sound recognition in an audio surveillance application. The GLCM captures the distribution of co-occurring values at a given offset. Unlike most other similar research which derive features from the GLCM, we use the matrix values itself to form the feature vector with analysis carried out in subbands. When compared to a baseline feature from related work, the proposed spectrogram image texture feature (SITF) gives marginally lower results under clean and high signal-to-noise ratio (SNR) conditions but significantly better results are achieved at low SNR, where the baseline feature was seen to be less effective.

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.

In this letter, we propose an efficient one-nearest-neighbor classifier of texture via the contrast of local energy histograms of all the wavelet subbands between an input texture patch and each sample texture patch in a given training set. In particular, the contrast is realized with a discrepancy measure which is just a sum of symmetrized Kullback-Leibler divergences between the input and sample local energy histograms on all the wavelet subbands. It is demonstrated by various experiments that our proposed method obtains a satisfactory texture classification accuracy in comparison with several current state-of-the-art texture classification approaches.

This paper contains study and review of various techniques used for feature extraction and texture classification. The objective of study is to find technique or combination of techniques to reduce complexity, speed while increasing the accuracy at the same time. Here we are studying and reviewing the three feature extraction methods: Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter method. Also classification methods like KNN, SVM, evolving fuzzy neural network (efunn), genetic algorithm and higher-order statistics. These are used on the texture datasets Brodatz, CUReT, VisTex and OuTex for the experimental purpose. In this paper, we present a comparative study of image texture classification techniques which are very much help full for image classifications

Liver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws' method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k-nearest neighbors (k-NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k-NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws' texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons. (C) 2014 Elsevier B.V. All rights reserved., Liver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws’ method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k-nearest neighbors (k-NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k-NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws’ texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons.

As the sensitivity of the fractional differential algorithm for detail image texture extraction and the difficulty of the best fractional differential order fingding, a novel adaptive fractional differential method is proposed, which can adaptively select the fractional differential order according to the mask window size, definition of fractional differential equations, the composite sub-band gradient vector (CSGV) obtained from the sub-images through a wavelet decomposition of a texture image, and human visual property. The fractional differential operator mask based on G-L formula is designed and realized by employing the adaptive order. The evaluation parameters of image texture feature extraction such as the image information entropy and multi-scale structural similarity (MS-SSIM) are used for quantitative analysis of the extraction method in experiment The experiment results show that for grey texture image this method is able to extract image texture and edge details completely, which approximate the results of optimal fractional differential order and more satisfies human visual sense. It is an effective approach to extract fine texture features of images.

In this paper, turning workpiece surface images were captured by the image acquisition system composed of digital microscopy, high-resolution camera and computer. the 14 texture feature parameters based on gray level co-occurrence matrix (GLCM) were extracted by using the method of statistical analysis, The variation between each texture parameter and arithmetic mean deviation (Ra) was investigated, thus the roughness of turning workpiece surface was evaluated qualitatively, the relationship between texture characteristic parameters and roughness evaluation index Ra was analyzed by using multiple regression method, and the linear and nonlinear regression testing model were bulit up. The result shows that the two models have good detection effect, and the nonlinear model performance better than the linear model.

Texture classification is very important in remote sensing images, X-ray photos, cell image interpretation and processing, and is also the active research areas of computer vision, image processing, image analysis, image retrieval, and so on. As to spatial domain image, texture analysis can use statistical methods to calculate the texture feature vector. In this paper, we use the gray level co-occurrence matrix and Gabor filter feature vector to calculate the feature vector. For the feature vector classification under normal circumstances we can use Bayesian method, KNN method, BP neural network. In this paper, we use a statistical classification method which is based on SVM method to classify images. Image classification generally includes image preprocessing, image feature extraction, image feature selection and image classification in four steps. In this paper, we use a gray-scale image, by calculating the image gray level cooccurrence matrix and Gabor filtering method to get feature extraction, and then use SVM to training and classification. From the test results, it shows that the SVM method is the better way to solve the problem of texture features for image classification and it shows strong adaptability and robustness for image classification.