Local Histogram Research Articles

Plasma metabolomics and quantitative interstitial abnormalities in ever-smokers

BackgroundQuantitative interstitial abnormalities (QIA) are an automated computed tomography (CT) finding of early parenchymal lung disease, associated with worse lung function, reduced exercise capacity, increased respiratory symptoms, and death. The metabolomic perturbations associated with QIA are not well known. We sought to identify plasma metabolites associated with QIA in smokers. We also sought to identify shared and differentiating metabolomics features between QIA and emphysema, another smoking-related advanced radiographic abnormality.MethodsIn 928 former and current smokers in the Genetic Epidemiology of COPD cohort, we measured QIA and emphysema using an automated local density histogram method and generated metabolite profiles from plasma samples using liquid chromatography–mass spectrometry (Metabolon). We assessed the associations between metabolite levels and QIA using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, pack-years, and inhaled corticosteroid use, at a Benjamini–Hochberg False Discovery Rate p-value of ≤ 0.05. Using multinomial regression models adjusted for these covariates, we assessed the associations between metabolite levels and the following CT phenotypes: QIA-predominant, emphysema-predominant, combined-predominant, and neither- predominant. Pathway enrichment analyses were performed using MetaboAnalyst.ResultsWe found 85 metabolites significantly associated with QIA, with overrepresentation of the nicotinate and nicotinamide, histidine, starch and sucrose, pyrimidine, phosphatidylcholine, lysophospholipid, and sphingomyelin pathways. These included metabolites involved in inflammation and immune response, extracellular matrix remodeling, surfactant, and muscle cachexia. There were 75 metabolites significantly different between QIA-predominant and emphysema-predominant phenotypes, with overrepresentation of the phosphatidylethanolamine, nicotinate and nicotinamide, aminoacyl-tRNA, arginine, proline, alanine, aspartate, and glutamate pathways.ConclusionsMetabolomic correlates may lend insight to the biologic perturbations and pathways that underlie clinically meaningful quantitative CT measurements like QIA in smokers.

RANCANG BANGUN APLIKASI PENGENALAN WAJAH UNTUK SISTEM PRESENSI DI PRODI TEKNIK INFORMATIKA UNIVERSITAS ISLAM RADEN RAHMAT MALANG MENGGUNAKAN METODE LBPH DAN LIBRARY OPENCV BERBASIS ANDROID

The attendance system is a mechanism to record the attendance of students in each class they take. The attendance system serves as evidence of students' presence in the learning process. In its journey, Universitas Islam Raden Rahmat has been using manual attendance through attendance journals in the learning process. However, with technological advancements, the attendance process has followed this trend, one of which is face recognition. In this research, the researcher designed a face recognition application for the attendance system that can be implemented on mobile platforms, especially Android. This application using the Local Binary Pattern Histogram (LBPH) method and the OpenCV library. The objective of this application is to enhance the flexibility and efficiency of the current attendance system by eliminating the need to repeatedly retrieve attendance forms, preventing fraud, and reducing paper usage. By using the waterfall system development method, testing showed a face recognition accuracy of 70%.

Neural Network Classification in Javanese Handwriting Recognition using Projection Profile Histogram and Local Binary Pattern Histogram

Indonesia consists of various regional tribes, where each tribe has cultural diversity and some even have their own regional letters, like Javanese tribe has Javanese characters. Javanese letters consist of 20 basic letters called Nglegena script. Subject about Javanese language is delivered to elementary student until now aims to preserve Indonesian culture especially the Javanese. In this study, we present two feature extraction methods are Local Binary Pattern (LBP) and Profile Projection (PP). Neural Network (NN) chosen as classifiers for classifying 20 javanese letters Nglegena. Some digital image processing processes are carried out, are image inversion, dilation, denoising and skeletoning. The Javanese script dataset is taken from the Kaggle database with the name Aksara Jawa: Aksara Jawa Custom Dataset, consists of 2154 train images and 480 test images. The experiment were carried out in two models, Projection Profile Histogram - Neural Network (PPH-NN) and Local Binary Pattern Histogram - Neural Network (LBPH-NN). The experiment show that both feature extraction methods have very good performance, 99.98% PPH-NN and 89.6% LBPH-NN on average.

Classification of Diseases on Corn Stalks using a Random Forest based on a Combination of the Feature Extraction (Local Binary Pattern and Color Histogram)

Corn disease has a significant impact on both the food industry and the yield of corn crops since corn serves as a fundamental and essential source of nutrition, especially for vegetarians and vegans. Therefore, ensuring the quality of corn is crucial, and to achieve this, protection against various diseases is necessary. Consequently, there is a pressing demand for an automated method capable of early-stage disease detection and prompt action. However, detecting diseases at an early stage poses a major challenge and is of utmost importance. This research focuses on the development of a classification model for corn stalk images using Random Forest. The model generates fine and coarse features of high quality to capture discriminative, boundary, pattern, and structural information used in the classification process. This research also utilizes the LBP (Local Binary Pattern) method and Color Histogram in the feature extraction process to obtain information related to texture and distinguishing patterns, that are employed in the classification process. Furthermore, the proposed model is evaluated using the corn plant image dataset, which was directly captured by the researcher in Madura, and consists of 3,000 data. The result of this research shows that the utilization of the proposed method can classify and identifying diseases in new data of digital images of corn stalks with an accuracy rate of 99.05%.

A low delay highly dynamic range infrared imaging system for complex scenes based on FPGA

We proposed a hardware-oriented framework for infrared images compression and enhancement. The aim is to achieve high-quality imaging with minimal delay. Specifically, we introduce several key improvements: Firstly, we adopt the anisotropic guided filtering for layering, which effectively addresses the issue of detail halo. Secondly, we proposed a dynamic range compression algorithm (LIHS&HRC), which is based on local information histogram statistics and thus highlights the gray scales with abundant details. Thirdly, we propose a low-complexity adaptive two-dimensional non-local mean algorithm, which preserves important details while reducing noise. Through extensive experimentation, it is demonstrated the proposed algorithm framework significantly reduces latency and Field Programmable Gate Array (FPGA) resource requirements. It generates high-quality infrared image sequences in a minimal delay of only 0.31 ms at a frame rate of 50fps, with consuming a mere 0.0362 MB of shift register and 0.0312 MB of BRAM.

Prototype of Melon Fruit Quality Sorter Based on Skin Texture Using Local Binary Pattern Histogram

Cucumis Melo L is one of the popular fruits in Indonesia. The numerous benefits and various contents of melon make it highly valuable for human health. Determining the quality of melon is crucial, considering its numerous benefits and contents. The quality of melon is determined by the presence of net-like patterns on the surface of the fruit's skin when it is ripe for harvest. Currently, conventional methods that rely on direct visual observation are still commonly used to sort the quality of melons. Therefore, a prototype system was developed to sort the quality of melons based on the texture of their skin. The purpose of this system is to reduce errors caused by eye fatigue and variations in accuracy during the sorting process. In this research, the feature extraction method of the local binary pattern (LBP) was employed, along with the K-Nearest Neighbor (KNN) method for classification. The classification was divided into two grades: Grade A and Grade B melons. The testing phase involved 200 data samples, with each grade consisting of 100 data samples. The results of the testing phase showed a success rate of 96% for the system. Based on the percentage, it can be concluded that the system has successfully performed well in sorting the quality of melons based on the texture of their skin.

Design and Analysis of an Expert System for the Detection and Recognition of Criminal Faces

The process of identifying a person using their facial traits is referred to as face recognition, and it is a form of biometric identification. The use of facial recognition might range from that of an entertainment tool to one of a security tool. Even while other forms of biometric identification, such as fingerprints and iris scans, are reliable, they require the active participation of an individual. As a result, criminals cannot rely on them as the most reliable means of verification. When a criminal database, which stores the individual details of a criminal, and facial recognition technology are brought together, it can identify a criminal who is depicted in an image or seen in a video feed. Not only does a criminal recognition system needs to have a high level of accuracy, but it also needs to be able to adapt to significant changes in lighting, occlusion, aging, expressions, and other factors. In this study, they were analyzed and compared with the many methods of face detection and face recognition, such as HAAR cascades, local binary patterns histogram, support vector machines, convolutional neural networks, and ResNet-34. These methods include a variety of different approaches to recognizing faces. An analysis of these strategies is also conducted and then put into practice to those that seem to be the most effective for the designed criminal recognition system. In addition to that, a variety of uses of this criminal recognition in the real world are also discussed.

The Use of Local Activation Timing Histogram in Ablation of Focal and Re-Entrant Atrial Tachycardias

BackgroundActivation mapping is often used to differentiate focal from re-entrant arrhythmias. This can be challenging but is critical to ablation success. The local activation time (LAT) histogram, which depicts point distribution over isochronal segments, may help characterize arrhythmia mechanisms and identify an optimal ablation strategy. ObjectivesThis study aimed to investigate features of the LAT histogram associated with the focal vs re-entrant mechanism of atrial tachycardias (ATs) and the use of the LAT histogram in the identification of target ablation sites. MethodsWe retrospectively evaluated cases of focal and re-entrant ATs performed at a single academic tertiary care center for which activation mapping was performed using CARTO 3 version 7 software (Biosense Webster). Baseline patient, arrhythmia, and procedural characteristics as well as LAT histogram features were evaluated for each case. LAT histogram–guided ablation targets were also compared against actual ablation sites. ResultsAmong 52 ATs assessed, 17 were focal, and 35 were re-entrant. Tachycardia cycle length was significantly shorter in re-entrant than in focal ATs (288.2 milliseconds [Q1-Q3: 250-306.5 milliseconds] vs 370 milliseconds [Q1-Q3: 285-400 milliseconds], respectively; P = 0.006). LAT histograms contained more “valleys” in re-entrant than in focal ATs (3 [Q1-Q3: 2-4] vs 1 [Q1-Q3: 1-1]; P < 0.001). No focal ATs contained >2 and no re-entrant ATs contained <1 LAT valley(s). All successful ablation sites correlated with LAT histogram–suggested sites. ConclusionsLAT histograms can help distinguish focal from re-entrant Ats and identify effective ablation sites.

Robot Vision Pattern Recognition of the Eye and Nose Using the Local Binary Pattern Histogram Method

The local binary pattern histogram (LBPH) algorithm is a computer technique that can detect a person's face based on information stored in a database (trained model). In this research, the LBPH approach is applied for face recognition combined with the embedded platform on the actuator system. This application will be incorporated into the robot's control and processing center, which consists of a Raspberry Pi and Arduino board. The robot will be equipped with a program that can identify and recognize a human's face based on information from the person's eyes and nose. Based on the results of facial feature identification testing, the eyes were recognized 131 times (87.33%), and the nose 133 times (88.67%) out of 150 image data samples. From the test results, an accuracy rate of 88%, the partition rate of 95.23%, the recall of 30%, the specificity of 99%, and the F1-Score of 57.5% were obtained.

Privacy-Preserving Image Retrieval Based on Disordered Local Histograms and Vision Transformer in Cloud Computing

Frequent data breaches in the cloud environment have seriously affected cloud subscribers and providers. Privacy-preserving image retrieval methods can improve the security of cloud image retrieval; however, existing methods have limited accuracy on dynamically updated image databases and mobile lightweight devices. In this study, we propose a privacy-preserving image retrieval method based on disordered local histograms and vision transformer in cloud computing, by designing a multiple encryption method and transformer-based feature model to better mine the local feature value of encrypted images. Specifically, the user performs different value substitution, position substitution, and color substitution on the subblocks of the image to protect the image information. The cloud server extracts the unordered local histogram from the encrypted image and generates retrievable features using transformer. Experiments show that compared with similar CNN schemes, the retrieval accuracy of this method is improved by 8.5%, and the retrieval efficiency is improved by 54.8%.

Localizing Face Recognition with Haar-Cascade Classifier and LBPH using Python

This paper experimented with the combination of the Haar-Cascade Classifier and Local Binary Pattern Histogram in creating a Face Recognition System using Python programming language. Images were gathered using Python script from the 10persons selected while facing the camera. Each individual had 1000 photos that were used to train a model. Another Python script was created for training, identification and recognition. The overall efficiency rating of the model was 84% which denotes a practical recommendation for utilization. The findings of this study contribute as a reference for further development of face recognition with other image classification algorithms

Attendance Monitoring System using Machine Learning Algorithms

Facial expressions are vital in practically every industry in this digital age. One of the most popular biometrics is face recognition. It can be used for security, authentication, identification, and has many additional benefits. Face recognition systems can also be utilised in schools, colleges, and offices, among other places. This program aims to build a classroom plan that uses the concept of facial recognition as a reality manual presence system. It is expensive and difficult to maintain. And there may be opportunities to become a representative to be present. This program contains four phases- database construction, face detection, facial recognition, review of attendees. After face identification, the Haar-Cascade Section and Local Binary Histogram pattern method sequence is used to detect faces

Accuracy Assessment of Atmospheric Correction of KMSS-2 Meteor-M #2.2 Data over Northern Eurasia

Atmospheric correction of satellite remote sensing data is a prerequisite for a large variety of applications, including time series analysis and quantitative assessment of the Earth’s vegetation cover. It was earlier reported that an atmospherically corrected KMSS-M (Meteor-M #2) dataset was produced for Russia and neighboring countries. The methodology adopted for atmospheric correction was based on localized histogram matching of target KMSS-M and MODIS reference gap-free and date-matching imagery. In this paper, we further advanced the methodology and quantitatively assessed Level-2 surface reflectance analysis-ready datasets, operatively produced for KMSS-2 instruments over continental scales. Quantitative assessment was based on accuracy, precision, and uncertainty (APU) metrics produced for red and near-infrared bands of the KMSS-2 instrument based on a reference derived from a MODIS MOD09 reconstructed surface reflectance. We compared error distributions at 5%, 20%, and 50% levels of cloudiness and indicated that the cloudiness factor has little impact on the robustness of the atmospheric correction regardless of the band. Finally, the spatial and temporal gradients of accuracy metrics were investigated over northern Eurasia and across different seasons. It was found that for the vast majority of observations, accuracy falls within the −0.010–0.035 range, while precision and uncertainty were below 0.06 for any band. With the successful launch of the most recent Meteor-M #2.3 with a new KMSS-2 instrument onboard, the efficiency and interoperability of the constellation are expected to increase.

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus. In copy-move forgery, the assailant intends to hide a portion of an image by pasting other portions of the same image. The detection of such manipulations in images has great demand in legal evidence, forensic investigation, and many other fields. The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated, even if the test image is subjected to attacks such as JPEG compression, scaling, rotation, and brightness variation. CoMoFoD, CASIA, and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms. The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.

Aplikasi Pembeda Daging Sapi dan Babi dengan Metode Color Moment dan Local Binary Pattern Histogram

Meat is one of the main food ingredients consumed by humans because it contains a lot of high protein, so it can increase intelligence and increase the stamina that humans need to carry out activities of daily life. Due to the very high level of meat consumption, these meats are often found in the market. The price of pork is cheaper than beef. The price difference between the two meats has led to the emergence of fraudulent practices in the beef trade. To solve this problem, an application to distinguish beef and pork can be designed to help socialize to the public about how to distinguish beef and pork. In this study, color characteristics will be used to distinguish pork and beef, because from previous studies the color characteristics have a higher accuracy. The method that can be used to extract features from meat images is the color moment method. Color feature extraction consists of Mean, Standard Deviation, and Skewness features. Meanwhile, to carry out the process of detecting the type of meat, the Local Binary Pattern Histogram (LBPH) method will be used. LBPH is a technique of the Local Binary Pattern (LBP) method to change the performance of object recognition results. The result of this research is an application that can be used to distinguish beef and pork. The process of recognizing the type of meat using the Color Moment and LBPH methods has a high success rate so that the Color Moment and LBPH methods can be applied to detect the type of meat, with a success rate of 99.33%.

Smart CCTV Detection Using Local Binary Pattern Histogram (LBPH)

Smart CCTV Detection Using Local Binary Pattern Histogram (LBPH)

Face Recognition Based on Computer Vision: A New Way of Attendance Marking

Computer Vision marked a significant contribution to the application of automated attendance marking. This technology captures the face of an individual facing in an image-capturing device such as a camera. This study developed a face recognition attendance marking based onthe machine learning method. Computer Vision libraries from OpenCV such as Haar Cascade Classifier and Local Binary Pattern Histogram were used for identification and recognition. Images were collected from 8 participating individuals. The images were clustered according to their name (label). These images were used for training to create a face recognition model. Real-time testing was performed to evaluate the system’s performance. The results generated a mean recognition accuracy of 95% which implies a significant basis for the application of the system to attendance marking

FACE RECOGNITION MENGGUNAKAN METODE HAAR CASCADE CLASSIFIER DAN LOCAL BINARY PATTERN HISTOGRAM

Increasing threats to data security is a consequence of technological advances that continue to develop, especially with the existence of technology that allows data access remotely. It is important to always maintain data security and take preventive measures to prevent data theft. The author proposes the use of facial recognition biometric technology. Face Recognition is one of several biometric technologies that can be used for identity verification systems. The data used amounted to 5 classes, classes with each class having 250 facial images, the total data used amounted to 1250 facial images, consisting of 1000 training data (train) and 250 test data ( tests). The shooting process is done automatically, the system automatically takes 250 faces per class. The next stage is feature extraction using the Local Binary Patterns Histograms (LBPH) method. Furthermore, the system will recognize the detected faces with those in the dataset. The use of the Haar Cascade Classifier and Local Binary Pattern Histogram (LBPH) methods to detect faces using 250 test data obtains an accuracy value of 92%. Testing in real time was carried out 75 times with a distance of 30 cm, 50 cm and 100 cm. The use of the Haar Cascade Classifier and Local Binary Pattern Histogram (LBPH) methods to detect faces in real time obtains an accuracy of 90%.

Accurate prediction of global-density-dependent range-separation parameters based on machine learning.

In this work, we develop an accurate and efficient XGBoost machine learning model for predicting the global-density-dependent range-separation parameter, ωGDD, for long-range corrected functional (LRC)-ωPBE. This ωGDDML model has been built using a wide range of systems (11 466 complexes, ten different elements, and up to 139 heavy atoms) with fingerprints for the local atomic environment and histograms of distances for the long-range atomic correlation for mapping the quantum mechanical range-separation values. The promising performance on the testing set with 7046 complexes shows a mean absolute error of 0.001 117 a0-1 and only five systems (0.07%) with an absolute error larger than 0.01 a0-1, which indicates the good transferability of our ωGDDML model. In addition, the only required input to obtain ωGDDML is the Cartesian coordinates without electronic structure calculations, thereby enabling rapid predictions. LRC-ωPBE(ωGDDML) is used to predict polarizabilities for a series of oligomers, where polarizabilities are sensitive to the asymptotic density decay and are crucial in a variety of applications, including the calculations of dispersion corrections and refractive index, and surpasses the performance of all other popular density functionals except for the non-tuned LRC-ωPBE. Finally, LRC-ωPBE (ωGDDML) combined with (extended) symmetry-adapted perturbation theory is used in calculating noncovalent interactions to further show that the traditional abinitio system-specific tuning procedure can be bypassed. The present study not only provides an accurate and efficient way to determine the range-separation parameter for LRC-ωPBE but also shows the synergistic benefits of fusing the power of physically inspired density functional LRC-ωPBE and the data-driven ωGDDML model.

Image Enhancement using Local Histogram Matching with Normal Distribution

Image Enhancement using Local Histogram Matching with Normal Distribution