Local Binary Pattern Technique Research Articles

A Hybrid Model for Household Waste Sorting (HWS) Based on an Ensemble of Convolutional Neural Networks

The exponential increase in waste generation is a significant global challenge with serious implications. Addressing this issue necessitates the enhancement of waste management processes. This study introduces a method that improves waste separation by integrating learning models at various levels. The method begins with the creation of image features as a new matrix using the Multi-Scale Local Binary Pattern (MLBP) technique. This technique optimally represents features and patterns across different scales. Following this, an ensemble model at the first level merges two Convolutional Neural Network (CNN) models, with each model performing the detection operation independently. A second-level CNN model is then employed to obtain the final output. This model uses the information from the first-level models and combines these features to perform a more accurate detection. The study’s novelty lies in the use of a second-level CNN model in the proposed ensemble system for fusing the results obtained from the first level, replacing conventional methods such as voting and averaging. Additionally, the study employs an MLBP feature selection approach for a more accurate description of the HW image features. It uses the Simulated Annealing (SA) algorithm for fine-tuning the hyperparameters of the CNN models, thereby optimizing the system’s performance. Based on the accuracy metric, the proposed method achieved an accuracy of 99.01% on the TrashNet dataset and 99.41% on the HGCD dataset. These results indicate a minimum improvement of 0.48% and 0.36%, respectively, compared to the other methods evaluated in this study.

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.

Effective prediction of human skin cancer using stacking based ensemble deep learning algorithm

ABSTRACT Automated diagnosis of cancer from skin lesion data has been the focus of numerous research. Despite that it can be challenging to interpret these images because of features like colour illumination changes, variation in the sizes and forms of the lesions. To tackle these problems, the proposed model develops an ensemble of deep learning techniques for skin cancer diagnosis. Initially, skin imaging data are collected and preprocessed using resizing and anisotropic diffusion to enhance the quality of the image. Preprocessed images are fed into the Fuzzy-C-Means clustering technique to segment the region of diseases. Stacking-based ensemble deep learning approach is used for classification and the LSTM acts as a meta-classifier. Deep Neural Network (DNN) and Convolutional Neural Network (CNN) are used as input for LSTM. This segmented images are utilized to be input into the CNN, and the local binary pattern (LBP) technique is employed to extract DNN features from the segments of the image. The output from these two classifiers will be fed into the LSTM Meta classifier. This LSTM classifies the input data and predicts the skin cancer disease. The proposed approach had a greater accuracy of 97%. Hence, the developed model accurately predicts skin cancer disease.

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%.

Improved Fuzzy Based Segmentation with Hybrid Classification for Skin Disease Detection

In this paper, we present a novel approach for the early detection of skin disorders, aiming to improve the prediction accuracy and precision. Our motivation lies in addressing the challenges posed by the uncertainty in skin lesion features. To achieve this, we employ fuzzy-based segmentation techniques to enhance the quality of skin disorder image processing. Additionally, we explore the integration of intensity data to further refine the segmented images. Our methodology consists of three main steps: segmentation, feature extraction, and classification. We begin by inputting the image into the segmentation phase, followed by the application of a fuzzy c-means clustering process to improve the segmentation output. From the resulting segmented image, we extract features using local binary pattern and local Gabor XOR pattern techniques. These features are then subjected to classification, employing a hybrid classifier that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The use of this hybrid classifier is particularly significant, as it leverages the strengths of both CNN and LSTM to enhance the predictive power of our model. Through rigorous experimentation, we have achieved promising results. Our proposed method exhibits an impressive accuracy of 94.6% and precision of 95.5% in comparison to the Conventional FCM Model, which only achieves an accuracy of 87% and precision of 82.5%.

Local binary pattern and its derivatives to handwriting-based gender classification

Several studies by psychologists and computer scientists have verified the link between handwriting and writer gender. The texture of the writing image is a major indicator of whether it is male or female writing. This paper conducts a comparison analysis to examine the effectiveness of various local binary patterns (LBPs) techniques in detecting gender from scanned images of handwriting. We study different LBP variants, including complete local binary pattern (CLBP), local ternary pattern (LTP), local configuration pattern (LCP), rotated local binary pattern (RLBP), local binary pattern variance (LBPV), and multi-scale local binary pattern (MLBP), as features for representing handwriting images. A support vector machine (SVM) is trained using features from male and female writing. The method achieves encouraging classification rates of 76.68 when tested on subsets of the Qatar University writer identification (QUWI) dataset containing English and Arabic writing samples when using the experimental protocols of the International Conference on Document Analysis and Recognition (ICDAR) 2013 gender classification competitions.

Enhancing Attendance Management through Real-Time Face Recognition using combination of LBP Histogram and Linear Discriminant Analysis

Face recognition technology's excellent accuracy and non-intrusiveness have made it a major player in the attendance management space. This research study presents an advanced method of tracking attendance through the integration of a Face Recognition System (FRS) with Linear Discriminant Analysis (LDA) and Local Binary Pattern (LBP) histogram analysis. The architecture of the suggested attendance system is described in the study, with an emphasis on how LDA and LBP techniques are used to increase the accuracy of face feature extraction and recognition. Through the use of LDA, the system improves face data's discriminative strength, producing attendance records that are more trustworthy. Robust facial texture and pattern identification are simultaneously made possible by LBP histogram analysis, particularly in a variety of environmental settings. To sum up, this study advances biometric attendance systems by demonstrating how LDA and LBP histogram analysis may improve the precision and dependability of face recognition systems used for attendance tracking

Swin-LBP: a competitive feature engineering model for urine sediment classification

Automated urine sediment analysis has become an essential part of diagnosing, monitoring, and treating various diseases that affect the urinary tract and kidneys. However, manual analysis of urine sediment is time-consuming and prone to human bias, and hence there is a need for an automated urine sediment analysis systems using machine learning algorithms. In this work, we propose Swin-LBP, a handcrafted urine sediment classification model using the Swin transformer architecture and local binary pattern (LBP) technique to achieve high classification performance. The Swin-LBP model comprises five phases: preprocessing of input images using shifted windows-based patch division, six-layered LBP-based feature extraction, neighborhood component analysis-based feature selection, support vector machine-based calculation of six predicted vectors, and mode function-based majority voting of the six predicted vectors to generate four additional voted vectors. Our newly reconstructed urine sediment image dataset, consisting of 7 distinct classes, was utilized for training and testing our model. Our proposed model has several advantages over existing automated urinalysis systems. Firstly, we used a feature engineering model that enables high classification performance with linear complexity. This means that it can provide accurate results quickly and efficiently, making it an attractive alternative to time-consuming and biased manual urine sediment analysis. Additionally, our model outperformed existing deep learning models developed on the same source urine sediment image dataset, indicating its superiority in urine sediment classification. Our model achieved 92.60% accuracy for 7-class urine sediment classification, with an average precision of 92.05%. These results demonstrate that the proposed Swin-LBP model can provide a reliable and efficient solution for the diagnosis, surveillance, and therapeutic monitoring of various diseases affecting the kidneys and urinary tract. The proposed model's accuracy, speed, and efficiency make it an attractive option for clinical laboratories and healthcare facilities. In conclusion, the Swin-LBP model has the potential to revolutionize urine sediment analysis and improve patient outcomes in the diagnosis and treatment of urinary tract and kidney diseases.

K-mean clustering and local binary pattern techniques for automatic brain tumor detection

Tumors in brains are caused by the unregulated emergence of tissue cells inside the brain. The early diagnosis and determining the precise location of the tumor in magnetic resonance imaging (MRI) and its size are essential for the teams of physicians. Image segmentation is often considered a preliminary step in medical image analyses. K-means clustering has been widely adopted for brain tumor detection. The result of this technique is a list of cluster images. The challenge of this method is the difficulty of selecting the appropriate cluster section that depicts the tumor. In this work, we analyze the influence of different image clusters. Each cluster is then split into the left and right parts. After that, the texture features are depicted in each part. Furthermore, the bilateral symmetry measure is applied to estimate the cluster that contains the tumor. Finally, the connected component labeling is employed to determine the target cluster for brain tumor detection. The developed technique is applied to 30 MRI images. The encouraging accuracy of 87% is obtained.

Performance Evaluation of Local Binary Patterns LBP for Copy-Move Forgery Detection in Digital Images: A Comparative Study

Copy-move forgery is a type of image tampering that involves copying a portion of an image and pasting it to another part of the same image with the intention of deceiving the viewer. In recent years, many approaches have been proposed to detect copy-move forgery, including those based on local binary patterns (LBP). In this paper, we perform a comprehensive evaluation of LBP-based methods for copy-move forgery detection using a dataset of 50 digital images. We compare the performance of four LBP-based methods, namely LBP, SIFT and SURF using metrics such as accuracy, precision, recall, and F1-score. Our results show that LBP outperforms the other methods in terms of accuracy and F1-score, while SIFT has the highest precision and recall. We also investigate the effect of various parameters, such as patch size and threshold values, on the performance of LBP. Our study provides valuable insights into the strengths and weaknesses of LBP-based methods for copy-move forgery detection, which can guide future research in this area. This study evaluates the performance of Local Binary Patterns (LBP) for detecting copy-move forgery in digital images. LBP is a widely used feature extraction technique in image processing and has been applied to various computer vision tasks, including forgery detection. The comparative study involves analyzing the accuracy, precision, recall, and F1-score of LBP and other popular forgery detection techniques, including SIFT and SURF, using a dataset of 50 digital images. The results show that LBP performs better than the other techniques, achieving an accuracy of 96.6%, precision of 94.0%, recall of 100%, and F1-score of 96.9%. This study provides useful insights for researchers and practitioners in the field of forgery detection, particularly for those interested in using LBP as a feature extraction technique.

Performance Evaluation of Fusion Based Efficient Algorithm for Facial Expression Recognition

Objectives: To develop face expression recognition system using JAFFE database and to evaluate the performance of the face expression recognition models. Methods: This study used the FER model based on modified-HoG (Histogram of oriented gradient), LBP (Local Binary Patterns) and Fast Key point detector and BRIEF descriptor (FKBD) to extract the significant features of JAFFE dataset. The features extracted using HoG, LBP and FKBD techniques form a feature vector. Then, the fusion of all the features is carried out at the feature level. The multiclass SVM and KNN classifiers are used to recognize the facial expressions, effectively. Findings: In this work, an effort is made to develop a robust FER model using JAFFE database. It is recorded that, based on the experimental results, the proposed model suits better with a performance rate of 98.26% for SVM and 96.51% for KNN, when compared with the different state-of-the-art methods. Novelty: Many FER models have been developed and adopted for enhancing their quality and to extract the facial features using transform and frequency domains. It is observed that, maximum approaches are based on generating the texture features. The fusion at the feature level using modified HoG, LBP and FKBD is performed and the SVM model is more compatible when compared with other classifiers and it supports one-to-one and one-to-many comparisons’ technique. Keywords: Face Expression Recognition; Local Binary Pattern; Emotions; Nearest Neighbor; Histogram of Gradients

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.

Comparing Local Binary Pattern and Gray Level Co-occurrence Matrix for Feature Extraction in Diabetic Retinopathy Classification

Diabetic retinopathy, a condition characterized by retinal damage and vision loss, is a prevalent complication of diabetes arising from elevated blood sugar levels. With a growing number of individuals affected, efficient and accurate diagnosis is crucial. This study aims to implement and compare the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) feature extraction techniques, which have demonstrated success in prior research. The comparison will provide a comprehensive under- standing of the image features, extract relevant data, and improve the performance of the image analysis pipeline for diabetic retinopathy classification. The result showed that from three scenarios the best accuracy provided by Support Vector Machine with the accuracy score between 73% until 74%, however, other algorithm have little difference which the result on 73%.

Multi-class EEG signal classification with statistical binary pattern synergic network for schizophrenia severity diagnosis

<abstract> <p>Electroencephalography (EEG) is a widely used medical procedure that helps to identify abnormalities in brain wave patterns and measures the electrical activity of the brain. The EEG signal comprises different features that need to be distinguished based on a specified property to exhibit recognizable measures and functional components that are then used to evaluate the pattern in the EEG signal. Through extraction, feature loss is minimized with the embedded signal information. Additionally, resources are minimized to compute the vast range of data accurately. It is necessary to minimize the information processing cost and implementation complexity to improve the information compression. Currently, different methods are being implemented for feature extraction in the EEG signal. The existing methods are subjected to different detection schemes that effectively stimulate the brain signal with the interface for medical rehabilitation and diagnosis. Schizophrenia is a mental disorder that affects the individual's reality abnormally. This paper proposes a statistical local binary pattern (SLBP) technique for feature extraction in EEG signals. The proposed SLBP model uses statistical features to compute EEG signal characteristics. Using Local Binary Pattern with proposed SLBP model texture based on a labeling signal with an estimation of the neighborhood in signal with binary search operation. The classification is performed for the earlier-prediction shizophrenia stage, either mild or severe. The analysis is performed considering three classes, i.e., normal, mild, and severe. The simulation results show that the proposed SLBP model achieved a classification accuracy of 98%, which is ~12% higher than the state-of-the-art methods.</p> </abstract>

Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

<p><span>The face recognition process is widely studied, and the researchers made great achievements, but there are still many challenges facing the applications of face detection and recognition systems. This research contributes to overcoming some of those challenges and reducing the gap in the previous systems for identifying and recognizing faces of individuals in images. The research deals with increasing the precision of recognition using a hybrid method of moments and local binary patterns (LBP). The moment technique computed several critical parameters. Those parameters were used as descriptors and classifiers to recognize faces in images. The LBP technique has three phases: representation of a face, feature extraction, and classification. The face in the image was subdivided into variable-size blocks to compute their histograms and discover their features. Fidelity criteria were used to estimate and evaluate the findings. The proposed technique used the standard Olivetti Research Laboratory dataset in the proposed system training and recognition phases. The research experiments showed that adopting a hybrid technique (moments and LBP) recognized the faces in images and provide a suitable representation for identifying those faces. The proposed technique increases accuracy, robustness, and efficiency. The results show enhancement in recognition precision by 3% to reach 98.78%.</span></p>

A novel image segmentation utilizing FUZZY-based LBP and active contour model

ABSTRACT This work presents an object recognition algorithm that combines local binary pattern (LBP) based on fuzzy logic approach and active contour model for segmenting different images to detect textured objects. Initially, images containing objects are segmented using the fuzzy logic-optimized LBP method. Then, we eliminate the image noise. Finally, utilizing a Chan-Vese active contour method, the target object of the image is highlighted. The segmentation has been compared with the classical LBP technique and the results indicated higher accuracy and quality for highlighting the object from the background. The classification of highlighted objects is performed with a convolution neural network (CNN). To authenticate the proposed approach, 140 images with the classification of 10 different objects were used. The simulation depicted that the proposed method has better results than other methods both in terms of segmentation error and performance. Significantly, CNN classification also showed a classification accuracy of 92.8%.

Marked and unmarked speed bump detection for autonomous vehicles using stereo vision

A current challenge for autonomous vehicles is the detection of irregularities on road surfaces in order to prevent accidents; in particular, speed bump detection is an important task for safe and comfortable autonomous navigation. There are some techniques that have achieved acceptable speed bump detection under optimal road surface conditions, especially when signs are well-marked. However, in developing countries it is very common to find unmarked speed bumps and existing techniques fail. In this paper a methodology to detect both marked and unmarked speed bumps is proposed, for clearly painted speed bumps we apply local binary patterns technique to extract features from an image dataset. For unmarked speed bump detection, we apply stereo vision where point clouds obtained by the 3D reconstruction are converted to triangular meshes by applying Delaunay triangulation. A selection and extraction of the most relevant features is made to speed bump elevation on surfaces meshes. Results obtained have an important contribution and improve some of the existing techniques since the reconstruction of three-dimensional meshes provides relevant information for the detection of speed bumps by elevations on surfaces even though they are not marked.

Automated Fruit Quality Detection Using Image Processing

In this paper, an automated fruit quality inspection and sorting system for apples has been proposed. The objective of this system is to replace the manual inspection system. This helps to speed up the process, improve the accuracy and efficiency of the system. In this work, the multiple images of apple are captured by using camera place above the conveyor belt. We have captured multiple images to make the proposed system more accurate which covers the maximum surface area of fruit. Then various image processing algorithms are used to extract the features of fruit such as texture, color, and size. Automated sorting and grading are done based on these features. The defected apple is diverted from conveyor using motor. The proposed embedded system works at higher speed and gives high accuracy in grading with low-cost solution. It will have a good prospect of application in fruit quality detection and grading areas. In the proposed algorithm, we have used k-means algorithm for segmentation and local Binary Pattern technique is used extract feature vectors of the test and training images. Then, the training and testing features are given to neural network algorithm to classify the test image into normal and abnormal class. We have considered the normal and abnormal (rotten apple images) for training purposes. Experimental results show that the proposed system accurately classifies the apple and gives better accuracy.

Automated Fruit Quality Detection Using Image Processing

In this paper, an automated fruit quality inspection and sorting system for apples has been proposed. The objective of this system is to replace the manual inspection system. This helps to speed up the process, improve the accuracy and efficiency of the system. In this work, the multiple images of apple are captured by using camera place above the conveyor belt. We have captured multiple images to make the proposed system more accurate which covers the maximum surface area of fruit. Then various image processing algorithms are used to extract the features of fruit such as texture, color, and size. Automated sorting and grading are done based on these features. The defected apple is diverted from conveyor using motor. The proposed embedded system works at higher speed and gives high accuracy in grading with low-cost solution. It will have a good prospect of application in fruit quality detection and grading areas. In the proposed algorithm, we have used k-means algorithm for segmentation and local Binary Pattern technique is used extract feature vectors of the test and training images. Then, the training and testing features are given to neural network algorithm to classify the test image into normal and abnormal class. We have considered the normal and abnormal (rotten apple images) for training purposes. Experimental results show that the proposed system accurately classifies the apple and gives better accuracy.

Classification of breast cancer images using completed local ternary pattern and support vector machine

Breast cancer is the major cause of deaths in women compared to other cancers. Though early detection of breast cancer reduces cancer deaths, it is a challenging task for physicians. Local binary pattern (LBP) and local ternary pattern (LTP) techniques are widely applied in texture classification applications. Since LBP is more sensitive to noise in texture classification, it needs to be improved for achieving better results. Though LTP is more robust to noise, there are few drawbacks. Completed LBP and completed local binary count techniques achieve good accuracy for texture classification, but they inherit few drawbacks of LBP. In this paper, completed LTP operator is applied on breast cancer images for better classification accuracy than LBP and completed LBP operators, by extracting sign and magnitude components. Experimental results based on breast cancer database show that the proposed technique achieved better classification accuracy than existing similar approaches.