LBP Features Research Articles

Alzheimer’s disease (AD) classification using MRI: A deep ensemble model with modified local pattern feature set

The Alzheimer disease (AD) is a neurologic brain condition, which affects the cells in the brain and eventually renders a patient incapable of performing routine daily tasks. Due to the outstanding spatial clarity, high access, and strong contrast, MRI has been utilized in analyses pertaining to AD. This work develops an AD classification model using MRI images. Here, preprocessing is done by the Gabor filter. Subsequently, the Improved U-net segmentation model is employed for image segmentation. The features extracted comprises of modified LGXP features, LTP features, and LBP features as well. Finally, the Deep ensemble classifier (DEC) model is proposed for AD classification which combines classifiers such as RNN, DBN, and Deep Maxout Network (DMN). For enhancing the efficiency for classification of AD, the optimal weight of DMN is adjusted using the Self Customized BWO (SC-BWO) model. The outputs from DEC are averaged and the final result is obtained. Finally, the analysis of dice, Jaccard scores is performed to show the betterment of the SC-BWO scheme.

Gabor, LBP, and BSIF features: Which is more appropriate for finger-knuckles-print recognition?

Gabor, LBP, and BSIF features: Which is more appropriate for finger-knuckles-print recognition?

Implementation of HSI and LBP Feature Extraction for Identification of Broccoli Quality with F-KNN

Implementation of HSI and LBP Feature Extraction for Identification of Broccoli Quality with F-KNN

Identification of Spinal Disorders Based on Spine X-ray Dataset Processing Using the LBP and CNN Algorithms

This research will use deep learning in conducting spinal x-ray image analysis but computational time problems are a problem of this study. Computations on deep learning across multiple nodes can increase training time and longer computation time compared to machine learning models. Based on experimental results, the best spine x-ray image classification results when using the CNN model with accuracy at the training stage, evaluation stage and test stage were 69.00%, 83.33% and 81.16% respectively. CNN models optimized with LBP get the lowest accuracy, with results at the training stage of 62.64%, validation stage of 75.00% and testing stage of 65.22%. LBP feature extraction turns out to have several drawbacks when combined with the CNN model, one major drawback is its inability to process global spatial information while retaining local texture information which causes LBP to be unable to capture the entire structure or context of the image, focusing only on local patterns so that many features of the image are lost. Another issue is the sensitivity of CNNs to image data, which can affect classification accuracy.

A Comparative Study to Analyze the Effect of Lumbar Stabilization and Conventional Back Extension Exercises on Mechanical Pain in Patients with Chronic Low Back Pain in Greater Noida

Lumbar discomfort is a major problem due to its harmful effects on health and high frequency in the general population. Depending on where and how long the pain lasts, there are three general categorizations for Lumbar discomfort: acute less than 6 weeks, sub-acute 6 to 12 weeks, and chronic more than 12 weeks. Several theories concerning the cause of nonspecific LBP feature poor posture, low body mass, hamstring flexibility, psychological discomfort, diminished trunk extensor endurance, and faulty muscular control of trunk. Pain in the lower back is a challenging illness which can result from injuries pertaining to the muscles and ligaments providing support vertebral column or from overuse or strain. One common treatment method for Lumbar discomfort is exercise therapy. This includes diverse forms of therapies including muscular strengthening, several forms of flexibility and stretching, and cardiovascular exercise for overall physical fitness. Normally, the stabilization system's job is to give the spine enough stability to meet the demands of changing circumstances immediately due to the posture changes with static and dynamic loads. As Per traditional understanding, spinal stabilization primarily relies on spinal biomechanics, the intervertebral disc, spinal ligaments, and osseous components. The stabilization and mobility of the lumbar spine can be facilitated by any muscle that exits the lumbar region. However, according to certain research, the lumbar multifidus and transverses abdominis muscles are very crucial for the stability of the lumbar segment. There is preliminary data indicating that those with long-term Lumbar discomfort do not properly recruit their core muscles and display core weakness13. Standard Exercise for back extension and exercises for lumbar stability both are used to treat low back discomfort. Thus, an effort is undertaken to compare and investigate the benefits of traditional back extension exercises and lumbar stabilization activities in patients with chronic Lumbar discomfort.

Improving Face Recognition Accuracy through Optimization of Haar and LBP Features in MATLAB

As we hasten into the digital age, facial recognition technology has emerged as a pivotal innovation across various domains such as security authentication, surveillance, and identity verification. This research delves into and enhances the Convolutional Neural Network (CNN) framework within the MATLAB environment, substantially augmenting the efficacy of facial recognition algorithms. The manuscript begins by tracing the evolution and current achievements within the facial recognition field, followed by an exploration into the theoretical foundation and key technologies of facial recognition. The aim of this study is to develop an advanced facial recognition algorithm based on CNN, employing efficient image preprocessing techniques such as grayscale conversion, noise reduction, and feature extraction, thereby significantly improving recognition accuracy and processing speed. Experiments conducted within MATLAB showcase the dual advancements in efficiency and speed offered by the optimized algorithm compared to traditional methods. Moreover, the paper discusses the adaptability of this algorithm in complex scenarios and the challenges and strategies likely to be encountered during pragmatic application. The outcomes of this research not only validate the practicality of the proposed algorithm but also illuminate directions and methodologies for the future exploration of facial recognition technology.

Facial expression recognition for wild dataset using LBP features and Random Forest Classifier

Facial expression recognition for wild dataset using LBP features and Random Forest Classifier

Application of “Artificial Intelligence + Education” Innovation Model in Higher Education Management and Student Training

Abstract The rapid development of modern science and technology promotes technological innovation in education, and this paper constructs the higher education innovation model of “Artificial Intelligence+Education” to realize the goal of responding to the construction of education informatization in higher education. The intelligent education platform is created using the theoretical foundation of three-layer education to manage data integratedly. The intelligent mode includes face recognition and voice recognition, which are realized using LBP feature extraction and neural network algorithms. Their effects are verified. The model is better than the traditional education model in all dimensions of the impact of student training. The P is less than 0.05, which is a significant difference, and the effect of education management is improved by 0.02~0.09. This paper’s “Artificial Intelligence + Education” innovation model has certain advantages in higher education management and student training, and provides a practical path to the construction of informationization and intelligent development of education. It provides a pragmatic path for constructing information and the thoughtful development of education.

Face Recognition Algorithm based on Optimal Fusion of LBP and HOG Features

Face Recognition Algorithm based on Optimal Fusion of LBP and HOG Features

Data Augmentation for Genetic Programming-Driven Late Merging of HOG and Uniform LBP Features for Texture Classification

The categorization of texture images requires the identification and extraction of meaningful keypoints, a crucial step in ensuring the precise representation of textured images. The literature has introduced numerous descriptors in order to detect and capture both local and global texture characteristics. These descriptors vary in their effectiveness depending on the specific application. However, it is generally accepted that they complement each other by compensating for their strengths and weaknesses. Therefore, many approaches focused on combining different types of image descriptors generating robust features that are invariant to image transformations. These solutions mostly focused on one way to deal with information and faced more problems as they need human intervention and a large set of data. The acknowledged benefits of combining multiple types of descriptors are accompanied by challenges specially. These challenges arise due to differences in properties, such as locality and sparsity, as well as the heterogeneity exhibited by generated features. To address this issue comprehensively, we present an approach that benefits from genetic programming techniques to generate and combine two distinct texture classifiers. It incorporates histograms of oriented gradients and local binary patterns descriptors, which capture different textures. To effectively fuse the results of both classifiers, the proposed approach employs a late fusion process along with data augmentation methods while using a limited amount of training data. To evaluate the performance of the proposed approach in texture image classification tasks, we have conducted extensive experiments on six challenging datasets encompassing various variations. We have also investigated its performance in a cross dataset problem where the model has been trained on instances of a dataset before being tested on samples of another dataset. The obtained results clearly demonstrate that the proposed approach surpasses other relevant low-level approaches, as well as existing GP-based and CNN methods specifically designed for describing and classifying textures. Thanks to its ability to simultaneously leverage multiple descriptors, the suggested solution shows a high potential for real-world applications, particularly in handling various image changes with robustness.

Physiological characteristics inspired hidden human object detection model

The current target detection algorithms provide the unsatisfactory performance on the task of detecting hidden human targets. In this study, we put forward the physiological characteristics inspired hidden human object detection model considering the spatio-temporal physiological features and their interdependent relationships. The experimental results of homemade hidden human object dataset demonstrate that the proposed model generates the detection accuracy of 64%, 44%, and 54% for indoor scene, outdoor scene, and overall dataset, respectively, outperforming the YOLO v4 models and the models based on HOG, LBP, and Haar features, with at least 22% promotion in detection accuracy. The ablation experiments indicate the effectiveness of each module of the method. In the future, the proposed model or the corresponding modeling idea has the potential to be applied to military rescue, public security investigation and other fields. Once the paper is accepted, we will make the homemade dataset publicly available.

Construction of college music information teaching mode under the background of Internet

Abstract The traditional music teaching method in the informationization era has been difficult to adapt to the needs of modern teaching and must be reformed in the direction of informationization. In this paper, based on the closure, inflection point and outer enclosing box features of the stroke line element, the recognition of handwritten notes is carried out from the three categories of straight line segments, folded line segments and quadratic curves. Meanwhile, for the binarized music score image, the multi-directional LBP features for spectral line detection are improved, and the computation method of multi-scale spectral line detection LBP features is established. The Manhattan distance is used to evaluate and select the features, which are inputted into XGBoost for classification and recognition training based on the statistical distribution characteristics of the features. Note recognition and spectral line recognition are applied to college music teaching, and the effectiveness of teaching is explored. In the rhythm-recognition path, the recognition teaching based on multi-scale and multi-directional LBP features led to an increase in students’ mastery of the musical score by 2.8 and in the phrasing and segmentation path by 3.5. Informational teaching led to a deepening of students’ mastery of the notes and musical scores.

Verifiable Privacy-Enhanced Rotation Invariant LBP Feature Extraction in Fog Computing

Rotation invariant local binary pattern (RI-LBP) features have been applied in diverse scenarios with the advantages of gray-scale and rotation invariance. Secure fog computing has become an emerging paradigm for enterprises or individuals with a huge volume of private data but limited computing power for feature extraction. Prior secure outsourcing protocols based on LBP and RI-LBP simply focus on local data privacy, which can only resist ciphertext-only attack, and also make extracted features exposed to the cloud. This work focuses on how to effectively ensure data confidentiality and feature integrity. We propose a verifiable privacy-enhanced protocol for RI-LBP feature extraction (VRLBP) based on the fog computing paradigm, which mitigates the aforementioned challenges by involving the proposed symmetric cryptographic scheme where local data and extracted features are proven secure against chosen plaintext attack. Meanwhile, the stage of verification can check the correctness of outsourced features with an overwhelming probability and constant computational complexity. The security analysis and computational costs demonstrate that VRLBP can reduce the computation overhead to around 30% of original feature extraction in a privacy-preserving manner. To exhibit the practical utility, VRLBP is implemented for deepfake detection on five public datasets. Extensive evaluations indicate that VRLBP achieves almost the same accuracy as the original RI-LBP algorithm and outperforms the state-of-the-art protocols.

Automated sugarcane crop disease forecasting with colour and texture features

ABSTRACT This paper intends to introduce a novel sugarcane plant disease prediction. There are three stages to the projected method, namely: (i) Preprocessing, (ii) Feature extraction, and (iii) Disease detection phase. Initially, the input image is given to the pre-processing stage in which the grey transformation and bilinear interpolation are carried out. The grey transformation is undergone to improve the clarity of the picture and the bilinear interpolation is used as a re-sampling technique. The feature extraction step follows the pre-processed picture, where both texture and colour features are retrieved. More specifically, the texture features like GLCM and suggested Distance-based LBP (D-LBP) features will be extracted. Subsequently, the extracted features are subjected to the disease detection phase process, where a hybrid classifier is introduced. This hybrid classifier blends the concepts of ‘Neural Network (NN) and Support Vector Machine (SVM)’ for better prediction results. In order to verify the effectiveness of the work that is suggested, a comparison analysis is conducted comparing the suggested and current approaches. The proposed method achieves highest accuracy in 80% of learning which is 13.97%, 16.12%, 11.82%, and 12.9% better than the other methods such as SVM, DT, RF, and NN, respectively.

Smoke recognition in steelmaking converter images: Class-distance-based feature selection model

Controlling the smoke and dust generated in steelmaking efficiently and precisely is a significant task for green metallurgy. This study aims at recognizing the smoke and dust generated from converters in steelmaking processes, to reach this goal, a research about feature generation and selection is conducted. The comprehensive feature datasets include GLCM features, LBP features and gray statistics features as a feature vector of 212 dimension for each image. To obtain better classification accuracy and performance, a supervised Class-Distance-based Feature Selection (CDFS) algorithm is proposed which involved the Mahalanobis distance which is chosen as class distance, correlation, T-test and train with machine learning classifier SVM. Then classifying images as smoke or non-smoke, the best feature combination with high classification performance as well as smoke images classification model can be exported as an output. Applied results on 5 converter image datasets show that this feature selection algorithm after optimization of Z-Score data standardization can not only reach an excellent accuracy in range of 92.54%-99.18% and AUC from 0.88 to 0.99 with feature size of 6–12 dimension, but apply well on small sample and imbalance class datasets. Compared with PSO, GA and BA, CDFS obtain higher accuracies as well as lower feature size. An application result also proved the effectiveness and generalization of this algorithm which means it can achieve precise classification accuracies with less cost in training as well. It can provide the foundation for efficient and precise emission treatment of converters in steelmaking.

Artificial intelligence-aided detection of rail defects based on ultrasonic imaging data

Railroads are one of society’s fundamental infrastructures, facilitating the transportation of passengers and goods over vast distances. Rail status data is immensely important for ensuring the safe and efficient operation of railroad networks. However, analyzing ultrasonic inspection data is a labor-intensive process and relies heavily on the expertise of experienced inspectors. To detect internal defects of the rail accurately and automatically, this paper proposes a customized image recognition method based on a convolutional neural network with limited B-scan rail image data collected within the industry. The proposed method uses EfficientNet-b7 as the backbone network to fully extract the B-scan rail image data features. With the help of transfer learning and data augmentation techniques, the backbone network is substantially enhanced so that it can understand high-level features of the object without being trained with large-scale B-scan image data. We establish a real-world internal rail defect dataset with 280 B-scan images and test our proposed method. The results reveal that the highest accuracy of the other mainstream CNN-based methods is 76.25% and the accuracy of the traditional method based on a support vector machine classifier trained with Tamura texture and LBP features is 60.00%. Our proposed EfficientNet-b7 model classifies rail defect B-scan images with an accuracy of 85.00%, precision of 84.71%, and recall of 85.00%. Compared to other rail internal defect detection methods, this method is more accurate. With the help of transfer learning and data augmentation, our proposed method achieves better performance and requires less data.

A temporal correlation coefficient model of grain temperature field cloud maps in horizontal warehouses

The temperature field cloud map (TFCM) is a color temperature contour map that can represent grain temperature distribution of bulk grain in a lagre storage facility (grain warehouse). The analysis of temporal variation characteristics of temperature field cloud maps can assist to analyze the changes of grain temperatures to monitor grain conditions changes. Wherein the temporal correlation coefficient is a type of variation characteristic of temperature field cloud maps at time domain. This research established a temporal correlation coefficients model of grain temperature for different grain warehouses storing paddy, wheat and maize in different provinces of China. That features of TFCM were extracted include the color coherence vector (CCV), LBP feature vector (LBP), gray level co-occurrence matrix vector (GLCMV), RGB feature vector (RGBV) and LAB feature vector (LABV), which were combined successively as the model input. The RMSEs between the correlation coefficients calculated by the model and that calculated by the measured grain temperatures in these planes were analyzed. The results show that the RMSEs of models with different vector combinations as input were different, and the RMSEs were all less than 0.001. Therefore, the five feature combinations could be used as the model input. Afterwards, distribution of temporal correlation coefficients of temperature field cloud maps from different warehouses were analyzed. The results show that in the XOY, XOZ and YOX planes of grain warehouses, temporal correlation coefficients (in the range (0.98, 1)) proportion of temperature field cloud maps were more than 95%, 85% and 82%, respectively.

Extraction and analysis of factors influencing college students’ mental health based on deep learning model

Abstract This paper analyzes the factors that affect the mental health of college students, and the focus of this analysis is on emotional-emotional factors. The extroverted presentation of emotional affects is used as visual information to study the mental health status of college students. Based on the advantages of long and short memory neural networks based on deep learning models in processing two-dimensional images, a computer vision task is used to perform visual recognition, target detection, and expression image classification of college students’ facial expressions. The use of video facial expression recognition with multi-feature fusion is utilized to effectively identify the facial expression machine of college students in both laboratory-controlled and outdoor environments. The mental health status of college students was analyzed in terms of facial expression recognition and feature extraction. The recognition rate for general features was 80.3%, 89.3% for six specific facial emotions, and 84.4% for LBP features.

Handwritten Devanagari Text Recognition using Single Classifier Approach with VSPCA Scheme

In this research paper we used individual classifier approach for Handwritten Devanagari text recognition. We experimented different categorical classifiers namely Random Forest Classifier (RFC), Support Vector Machine (SVM), K Nearest Neighbor Classifier (KNN), Logistic Regression Classifier (LogRegr), Decision Tree Classifier (DTree). Seven different feature sets are used namely Eccentricity, Euler Number, Horizontal Histogram, Vertical Histogram, HOG Features, LBP Features, and Statistical Features. The experimentation is carried out on 9434 different characters whose features are extracted from 220 handwritten image documents from PHDIndic_11 dataset. We deduced and implemented a unique scheme namely VSPCA scheme. VSPCA is Vectorization, Scaling, and Principal Component Analysis carried out on all feature sets before being given for model training. We obtained varied accuracies using all these five classifiers on all these six feature sets in which 99.52% highest accuracy is observed.

Research on face emotion recognition algorithm based on deep learning neural network

Abstract This paper explores face emotion recognition based on deep learning neural networks in order to make the computer recognize facial emotion more accurately. Firstly, we introduce the convolutional neural network, which consists of a convolutional layer, pooling layer and fully connected layer in the deep learning neural network, and propose the variational self-encoder face emotion recognition algorithm based on the convolutional neural network in combination with the facial emotion recognition process. The correct recognition rates achieved by EVA using 32×32 pixels, LBP features and 50-dimensional Gabor wavelet features are 95.13%, 96.74% and 98.84%, respectively. In terms of emotion recognition ability, the recognition accuracy percentages were around 70% for large emotion fluctuations and around 30% for small emotion fluctuations. Since the neutral facial emotion is not well distinguished, the recognition degree of neutral facial emotion is only 80%, and the recognition degree using the self-encoder-based face emotion recognition algorithm in several facial features extraction is above 80%, and the recognition degree with neutral emotion removed is up to about 90%. Therefore, the algorithm in this paper has good classification performance and can recognize and analyze facial emotion features accurately.