Center-symmetric Local Binary Pattern Research Articles

Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds

Asthma is a common disease. The clinical diagnosis is usually confirmed on a pulmonary function test, which is not always readily accessible. We aimed to develop a computationally lightweight handcrafted machine learning model for asthma detection based on cough sounds recorded using mobile phones. Toward this aim, we proposed a novel feature extractor based on a one-dimensional version of the published attractive-and-repulsive center-symmetric local binary pattern (1D-ARCSLBP), which we tested on a new cough sound dataset. We prospectively recorded cough sounds from 511 asthmatics and 815 non-asthmatic subjects (comprising mostly healthy volunteers), which yielded 1875 one-second cough sound segments for analysis. Our model comprised four steps: (i) preprocessing, in which speech signals and stop times (silent zones between coughs) were removed, leaving behind analyzable cough sound segments; (ii) feature extraction, in which tunable q-factor wavelet transformation was used to perform multilevel signal decomposition into wavelet subbands, allowing 1D-ARCSLBP to extract local low- and high-level features; (iii) feature selection, in which neighborhood component analysis was used to select the most discriminative features; and (iv) classification, in which a standard shallow cubic support vector machine was deployed to calculate binary classification results (asthma versus non-asthma) using tenfold and leave-one-subject-out cross-validations. Our model attained 98.24% and 96.91% accuracy rates with tenfold and leave-one-subject-out cross-validation strategies, respectively, and obtained a low-time complexity. The excellent results confirmed the feature extraction capability of 1D-ARCSLBP and the feasibility of the model being developed into a real-world application for asthma screening.

Unsupervised Change Detection in HR Remote Sensing Imagery Based on Local Histogram Similarity and Progressive Otsu

Unsupervised change detection of land cover in multispectral satellite remote sensing images with a spatial resolution of 2–5 m has always been a challenging task. This paper presents a method of detecting land cover changes in high-spatial-resolution remote sensing imagery. This method has three characteristics: (1) Extended center-symmetric local binary pattern (XCS-LBP) is used to extract image features to emphasize spatial context information in initial change detection. Then, spectral information is combined to improve the accuracy of change detection. (2) The local histogram distance of XCS-LBP features is used as the change vector to improve the expression of change information. (3) A progressive Otsu method is developed for threshold segmentation of the change vector to reduce the false detection rate. Four datasets with different landscape complexities and seven state-of-the-art unsupervised change detection methods were used to test the performance of the proposed method. Quantitative results showed that the proposed method reduced the false detection rate and improved the accuracy of the detection of land cover changes. The F1 score achieved by the proposed method reached 0.8688, 0.8867, 0.7725, and 0.6634, respectively, which are higher than the highest corresponding F1 score achieved by the benchmark methods (0.8533, 0.8549, 0.6545, and 0.5895, respectively).

Enhanced Face Detection in Videos Based on Integrating Spatial Features (LBP, CS-LBP) with CNN Technique

Face detection is a crucial aspect of computer vision and image processing, in order to enable the automatic detection and identification of human faces in video streams, face detection is an essential component of computer vision and image processing. Applications for facial recognition, video analytics, security systems, and surveillance all depend on it. Face identification techniques face many obstacles and issues, such as positional fluctuations, illumination changes, resolution and scale issues, facial emotions, and cosmetics. Robust algorithms are required for efficient face detection. This field looks at the feature extraction process using a variety of techniques. These consist of the center symmetric local binary patterns (CS_LBP) approach and the local binary patterns (LBP) method. The YouTube Face database provided the video frames that we used for our study. In order to train the convolutional neural network (CNN) to detect human faces in the video and draw a bounding box around them. The experimental results of the suggested approaches show that. The accuracy rate was 94% higher with the LBP techniques. However, the CS_LBP technique showed the best level of accuracy in both face detection and face rectangle recognition, with an accuracy rate of 95%.

Gabor Filter and Centre Symmetric-Local Binary Pattern based technique for forgery detection in images

Gabor Filter and Centre Symmetric-Local Binary Pattern based technique for forgery detection in images

Evaluating face recognition with different texture descriptions and convolution neural network

Extracting the remarkable attributes of the image objects is an issue of ongoing research special in the face recognition problem. This paper presents two directions. The first is a comparison between the local binary patterns (LBP) and its modified center symmetric LBP drawn from localized facial expressions and due to the efficiency, K-nearest neighbor (KNN) and the support vector machine (SVM) techniques play significant roles in this research used to implement the proposed system efficiently. The second direction proposes an efficient architecture by depending on deep learning convolution neural network (CNN) to implement face recognition. Such a design consists of two parts: a convolutional learning feature model and a classification model. The first one learns the important feature,while the second part produces a score class for each sample input. Many experiments are implemented on the known dataset once for the number of nearest neighbors (K value), and then decrease the number of expression samples for each individual the other time. The cross-validation method is used to provide a true picture of the accuracy of the face recognition system. In all experiment results, the center symmetric LBP with KNN outperforms the classic LBP. While significant progress in the results accuracy recognition ratio of the CNN model compared with other methods used.

Automated Adrenal Gland Disease Classes Using Patch-Based Center Symmetric Local Binary Pattern Technique with CT Images.

Incidental adrenal masses are seen in 5% of abdominal computed tomography (CT) examinations. Accurate discrimination of the possible differential diagnoses has important therapeutic and prognostic significance. A new handcrafted machine learning method has been developed for the automated and accurate classification of adrenal gland CT images. A new dataset comprising 759 adrenal gland CT image slices from 96 subjects were analyzed. Experts had labeled the collected images into four classes: normal, pheochromocytoma, lipid-poor adenoma, and metastasis. The images were preprocessed, resized, and the image features were extracted using the center symmetric local binary pattern (CS-LBP) method. CT images were next divided into 16 × 16 fixed-size patches, and further feature extraction using CS-LBP was performed on these patches. Next, extracted features were selected using neighborhood component analysis (NCA) to obtain the most meaningful ones for downstream classification. Finally, the selected features were classified using k-nearest neighbor (kNN), support vector machine (SVM), and neural network (NN) classifiers to obtain the optimum performing model. Our proposed method obtained an accuracy of 99.87%, 99.21%, and 98.81% with kNN, SVM, and NN classifiers, respectively. Hence, the kNN classifier yielded the highest classification results with no pathological image misclassified as normal. Our developed fixed patch CS-LBP-based automatic classification of adrenal gland pathologies on CT images is highly accurate and has low time complexity [Formula: see text]. It has the potential to be used for screening of adrenal gland disease classes with CT images.

Threshold center-symmetric local binary convolutional neural networks for bilingual handwritten digit recognition

The writing style of the same writer varies from instance to instance in Arabic and English handwritten digit recognition, making handwritten digit recognition challenging. Currently, deep learning approaches are applied in many applications, including convolutional neural networks (CNNs) modified to produce other models, such as local binary convolutional neural networks (LBCNNs). An LBCNN is created by fusing a local binary pattern (LBP) with a CNN by reformulating the LBP as a convolution layer called a local binary convolution (LBC). However, LBCNNs suffer from the random assignment of 1, 0, or -1 to LBC weights, making LBCNNs less robust. Nevertheless, using another LBP-based technique, such as center-symmetric local binary patterns (CS-LBPs), can address such issues. In this paper, a new model based on CS-LBPs is proposed called center-symmetric local binary convolutional neural networks (CS-LBCNN), which addresses the issues of LBCNNs. Furthermore, an enhanced version of CS-LBCNNs called threshold center-symmetric local binary convolutional neural networks (TCS-LBCNNs) is proposed, which addresses another issue related to the zero-thresholding function. Finally, the proposed models are compared to state-of-the-art models, proving their ability by producing a more accurate and significant classification rate than the existing LBCNN models. For the bilingual dataset, the TCS-LBCNN enhances the accuracy of the LBCNN and CS-LBCNN by 0.15% and 0.03%, respectively. In addition, the comparison shows that the accuracy acquired by the TCS-LBCNN is the second-highest using the MNIST and MADBase datasets.

IntramuscularEMGclassifier for detecting myopathy and neuropathy

AbstractThis article presents an automatic diagnostic system to classify intramuscular electromyography (iEMG) signals, thereby detecting neuromuscular disorders. To this end, we tailored the center symmetric local binary pattern (CSLBP) to analyze one‐dimensional (‐D) signals. In this approach, the ‐D CSLBP feature extracted from a decimated iEMG signal is fed to a combination of classifiers, which in turn assigns a set of labels to the signal, and ultimately the signal category is determined by the Boyer‐Moore majority voting (BMMV) algorithm. The proposed framework was investigated with a benchmark iEMG dataset that contains signals recorded from three different muscles:biceps brachii(BB),deltoideus(DE), andvastus medialis(VM). In a repeated ‐fold cross‐validation, CSLBP‐Combined‐Classifiers‐BMMV (CSLBP‐CC‐BMMV) achieved an average classification accuracy of %, %, and % for the iEMG signals recorded from BB, DE, and VM muscle, respectively. Interestingly, the performance of CSLBP‐CC‐BMMV surpassed the other published approaches and ensemble learning methods that are akin to our scheme in terms of classification accuracy and computational time.

Identification of Anomalies in Mammograms through Internet of Medical Things (IoMT) Diagnosis System.

Breast cancer is the primary health issue that women may face at some point in their lifetime. This may lead to death in severe cases. A mammography procedure is used for finding suspicious masses in the breast. Teleradiology is employed for online treatment and diagnostics processes due to the unavailability and shortage of trained radiologists in backward and remote areas. The availability of online radiologists is uncertain due to inadequate network coverage in rural areas. In such circumstances, the Computer-Aided Diagnosis (CAD) framework is useful for identifying breast abnormalities without expert radiologists. This research presents a decision-making system based on IoMT (Internet of Medical Things) to identify breast anomalies. The proposed technique encompasses the region growing algorithm to segment tumor that extracts suspicious part. Then, texture and shape-based features are employed to characterize breast lesions. The extracted features include first and second-order statistics, center-symmetric local binary pattern (CS-LBP), a histogram of oriented gradients (HOG), and shape-based techniques used to obtain various features from the mammograms. Finally, a fusion of machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA are employed to classify breast cancer using composite feature vectors. The experimental results exhibit the proposed framework's efficacy that separates the cancerous lesions from the benign ones using 10-fold cross-validations. The accuracy, sensitivity, and specificity attained are 96.3%, 94.1%, and 98.2%, respectively, through shape-based features from the MIAS database. Finally, this research contributes a model with the ability for earlier and improved accuracy of breast tumor detection.

Face Recognition Attendance System Method Based on Fusion of LBP and HOG

As one of the hot topics in the field of computer vision research, face recognition technology has received significant attention due to its potentiality for a wide range of applications in government as well as commercial purposes. In practical applications, although several existing face recognition methods have achieved good performances in specific scenes, they easily suffer from a sharp decline in recognition rate if affected by different conditions of light, expression, posture and occlusion. Among many factors, influences of complex illuminations on face recognition are particularly significant. To further improve the performance of the existing local binary pattern (LBP) operator, neighbourhood weighted average LBP (NWALBP) is first proposed for fully considering the strong correlations between pixel pairs in the neighbourhood, which extends the traditional LBP uni-layer neighbourhood template window to the bi-layer neighbourhood template window and calculates the weighted average of bi-layer neighbourhood pixels in each direction. Then, inspired by centre symmetric LBP (CS-LBP), centre symmetric NWALBP (CS-NWALBP) is further proposed, which can effectively reduce computation complexity by only comparing the weighted average values of the neighbourhood pixels that are symmetric about the centre pixel. Finally, by combining the merit of histogram of oriented gradient (HOG), a feature fusion algorithm named CS-NWALBP+HOG is suggested. Several experiments have eventually demonstrated that our proposed algorithms have more robust performance under complex illumination conditions if compared with many other latest algorithms.

Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning

Identification and diagnosis of leukemia earlier is a contentious issue in therapeutic diagnostics for reducing the rate of death among people with Acute Lymphoblastic Leukemia (ALL). The investigation of White Blood Cells (WBCs) is essential for the detection of ALL-leukaemia cells, for which blood smear images were being used. This study created an intelligent framework for identifying healthy blood cells from leukemic blood cells in blood smear images. The framework combines the features extracted by Center Symmetric Local Binary Pattern (CSLBP), Gabor Wavelet Transform (GWT), and Local Gradient Increasing Pattern (LGIP), the data was then fed into machine learning classifiers including Decision Tree (DT), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF)). As the training set, the ALL-IDB2 database was utilized to create a balanced database with 260 blood smear images. Consequently, to generate the optimum feature set, a recommended model was established by using numerous individual and combined feature extraction methodologies. The investigational consequences demonstrate that the developed feature fusion strategy surpassed previous existing techniques, with an overall accuracy of 97.49 ± 1.02% utilizing Ensemble classifier.

Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network

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

Computer vision based asphalt pavement segregation detection using image texture analysis integrated with extreme gradient boosting machine and deep convolutional neural networks

Aggregate segregation is a major form of defect that accelerates the pavement deterioration rate. Therefore, asphalt pavement segregation needs to be detected accurately and early during the quality survey process. This study proposes and verifies a computer vision based method for automatic identification of aggregate segregation. The new method includes Extreme Gradient Boosting Machine integrated with Attractive Repulsive Center-Symmetric Local Binary Pattern (ARCSLBP-XGBoost) and Deep Convolutional Neural Network (DCNN). Experimental results obtained from a repetitive random data sampling process with 20 runs show that the ARCSLBP-XGBoost is a capable approach for detecting asphalt pavement segregation with outstanding performance measurement metrics (classification accuracy rate = 0.95, precision = 0.93, recall = 0.98, and F1 score = 0.95).

Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach

During the phase of building survey, spalling and its severity should be detected as earlier as possible to provide timely information on structural heath to building maintenance agency. Correct detection of spall severity can significantly help decision makers develop effective maintenance schedule and prioritize their financial resources better. This study aims at developing a computer vision-based method for automatic classification of concrete spalling severity. Based on input image of concrete surface, the method is capable of distinguishing between a minor spalling in which the depth of the broken-off material is less than the concrete cover layer and a deep spalling in which the reinforcing steel bars have been revealed. To characterize concrete surface condition, image texture descriptors of statistical measurement of color channels, gray-level run length, and center-symmetric local binary pattern are used. Based on these texture-based features, the support vector machine classifier optimized by the jellyfish search metaheuristic is put forward to construct a decision boundary that partitions the input data into two classes of shallow spalling and deep spalling. A dataset consisting of 300 image samples has been collected to train and verify the proposed computer vision method. Experimental results supported by the Wilcoxon signed-rank test point out that the newly developed method is highly suitable for concrete spall severity classification with accuracy rate = 93.33%, F1 score = 0.93, and area under the receiver operating characteristic curve = 0.97.

A multimodal architecture using Adapt‐HKFCT segmentation and feature‐based chaos integrated deep neural networks (Chaos‐DNN‐SPOA) for contactless biometricpalm vein recognition system

In the recent past, the fusion of various unimodal biometrics has gained increasing attention from researchers dedicated to the use of practical biometrics. In this paper, a Chaos Integrated Deep Neural Networks (Chaos-DNN) using Sandpiper Optimization Algorithm (SPOA) is proposed to enhance the performance of the multimodal contactless biometric pattern recognition system. The most significant advantage of palm vein and palm dorsal vein recognition is that it provides relatively high accuracy, reliability and also liveness detection. In this recognition process, the palm and its dorsal vein images are segmented using multilevel segmentation of region of interest (ROI) depending on Adaptive Hyper Kernel Optimized Fuzzy Clustering Technique (Adapt-HKFCT), and then ROI extracted based on the valley points among the index and ring finger using the Knuckle points. Then, the features of the ROI amid the index and ring finger are extracted using centre-symmetric local binary pattern algorithm and fed to the Chaos-DNN for training and classification. The efficiency of the features is optimized using SPOA. The extracted feature points help in authenticating a person using his/her chosen biometric traits for security purposes. The objective of this system is to increase the efficiency of the biometric system, and the performance is calculated depending on equal error rate, correct recognition rate, decidability index, false acceptance rate and false rejection rate. The proposed algorithm has been implemented in MATLAB platform. The experimental outcomes demonstrate that the proposed palm vein recognition depending Adapt-HKFCT with Chaos-DNN using SPOA method (PVR-AHKFCT-CDNN-SPOA) attains higher performance metrics with an accuracy of 98.3% and is also more efficient likened to the other existing methods, such as K Nearest Neighbour with Particle Swarm Optimization (PVR-KNN-PSO), Fuzzy Brain Storm Optimization based on Adaptive Thresholding (PVR-FBSO-AT), multiscale local binary pattern with ant colony optimization (PVR-MSLBP-ACO) and Deep Learning with Bayesian Optimization (PVR-DL-BO).

An enhanced segmentation technique and improved support vector machine classifier for facial image recognition

PurposeOne of the challenging issues in computer vision and pattern recognition is face image recognition. Several studies based on face recognition were introduced in the past decades, but it has few classification issues in terms of poor performances. Hence, the authors proposed a novel model for face recognition.Design/methodology/approachThe proposed method consists of four major sections such as data acquisition, segmentation, feature extraction and recognition. Initially, the images are transferred into grayscale images, and they pose issues that are eliminated by resizing the input images. The contrast limited adaptive histogram equalization (CLAHE) utilizes the image preprocessing step, thereby eliminating unwanted noise and improving the image contrast level. Second, the active contour and level set-based segmentation (ALS) with neural network (NN) or ALS with NN algorithm is used for facial image segmentation. Next, the four major kinds of feature descriptors are dominant color structure descriptors, scale-invariant feature transform descriptors, improved center-symmetric local binary patterns (ICSLBP) and histograms of gradients (HOG) are based on clour and texture features. Finally, the support vector machine (SVM) with modified random forest (MRF) model for facial image recognition.FindingsExperimentally, the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy, similarity index, dice similarity coefficient, precision, recall and F-score results. However, the proposed method offers superior recognition performances than other state-of-art methods. Further face recognition was analyzed with the metrics such as accuracy, precision, recall and F-score and attained 99.2, 96, 98 and 96%, respectively.Originality/valueThe good facial recognition method is proposed in this research work to overcome threat to privacy, violation of rights and provide better security of data.

An enhanced LBP-based technique with various size of sliding window approach for handwritten Arabic digit recognition

Many variations of local binary pattern (LBP) were proposed to enhance its performance, including uniform local binary pattern (ULBP), center-symmetric local binary patterns (CS-LBP), center symmetric local ternary patterns (CS-LTP), center symmetric local multilevel pattern (CS-LMP), etc. In this paper, the accuracies of LBP technique and its variations are enhanced using four different sizes of a sliding window approach. This approach is used for investigating whether the features extracted by LBP are significant enough or its versions are needed as well. Five LBP-based techniques have been used including LBP, CS-LBP, CS-LTP, CS-LMP, and U2LBP. They have been applied to an Arabic digit image dataset called MAHDBase. Support vector machine (SVM) and random forests are utilized as classifiers. The experimental results show that the obtained accuracies have been improved by 19.56%, 21.43%, 5.63%, 6.51% and 5.62% for CS-LBP, CS-LMP, U2LBP, CS-LTP, and LBP, respectively, when the sliding window approach has been applied and SVM with linear kernel has been used as a classifier. Moreover, the results show that there is no need to use LBP variations to enhance the accuracy if the sliding window is applied because the highest accuracy has been acquired using LBP. At the end, the accuracy of proposed systems has been compared against other state-of-the-art LBP-based techniques showing the significance of the proposed systems.

Face Age Estimation Based on CSLBP and Lightweight Convolutional Neural Network

As the use of facial attributes continues to expand, research into facial age estimation is also developing. Because face images are easily affected by factors including illumination and occlusion, the age estimation of faces is a challenging process. This paper proposes a face age estimation algorithm based on lightweight convolutional neural network in view of the complexity of the environment and the limitations of device computing ability. Improving face age estimation based on Soft Stagewise Regression Network (SSR-Net) and facial images, this paper employs the Center Symmetric Local Binary Pattern (CSLBP) method to obtain the feature image and then combines the face image and the feature image as network input data. Adding feature images to the convolutional neural network can improve the accuracy as well as increase the network model robustness. The experimental results on IMDB-WIKI and MORPH 2 datasets show that the lightweight convolutional neural network method proposed in this paper reduces model complexity and increases the accuracy of face age estimations.

A Novel Reliable Approach For Image Batik Classification That Invariant With Scale And Rotation Using MU2ECS-LBP Algorithm

Abstract Batik in Indonesia has various types of patterns, which are arranged repeatedly to illustrate the basic motifs of cloth as a whole. Types of batik motifs collected through several sources such as batik magazines, the internet or directly using a digital camera. Batik’s pattern automatic classification still requires some improvement especially in regards to invariant with scale and rotation. The batik‘s pattern of changing this dilemma at the same time needs a feature extraction algorithm that is reliable in supporting image classification. This algorithm is multiwindow and multiscale extended center symmetric local binary patterns (MU2ECS-LBP) which uses several windows such as the size of 6x6, 9x9, 12x12, and 15x15 or a combination between windows. To recognize the batik pattern automatically, we implement a batik classification method using kNN and ANN. After doing some experiments the results of the accuracy values with the kNN method, based on the effect of training images and image conditions, on multi-window 6-9-12-15, another multi-window 6-12-15 with overlapping images of 40 and 50 pixels and the number of image classes 5, 9 and 12 classes are 99.91% and 99.8%. With the ANN method, the accuracy classification value is based on the multi-window effect and image overlap, where the highest accuracy value on multiwindow 6-9-12-15 with ANN architecture 64-240-12 is 98.43%. A novel algorithm is a development of the local binary pattern algorithm, but by looking at the results of classification accuracy which is very good and reliable. So that the feature extraction algorithm is very feasible to be developed and continued for other research.

An efficient method to classify GI tract images from WCE using visual words

The digital images made with the Wireless Capsule Endoscopy (WCE) from the patient's gastrointestinal tract are used to forecast abnormalities. The big amount of information from WCE pictures could take 2 hours to review GI tract illnesses per patient to research the digestive system and evaluate them. It is highly time consuming and increases healthcare costs considerably. In order to overcome this problem, the CS-LBP (Center Symmetric Local Binary Pattern) and the ACC (Auto Color Correlogram) were proposed to use a novel method based on a visual bag of features (VBOF). In order to solve this issue, we suggested a Visual Bag of Features(VBOF) method by incorporating Scale Invariant Feature Transform (SIFT), Center-Symmetric Local Binary Pattern (CS-LBP) and Auto Color Correlogram (ACC). This combination of features is able to detect the interest point, texture and color information in an image. Features for each image are calculated to create a descriptor with a large dimension. The proposed feature descriptors are clustered by K- means referred to as visual words, and the Support Vector Machine (SVM) method is used to automatically classify multiple disease abnormalities from the GI tract. Finally, post-processing scheme is applied to deal with final classification results i.e. validated the performance of multi-abnormal disease frame detection.