Journal articles on the topic 'OpenCV library too'

<strong>Abstract. </strong>Intensive water quality determination needs to be adjusted with technological developments to meet today's society's needs and increased water pollution due to urbanization. Therefore, early detection is essential for in site water quality determination and as a critical consideration in making health and environmental decisions. OpenCV is a library programming feature for Computer Vision which focuses on extracting information from images in real-time, this can be considered to be potential to measure the pollutant concentration. <strong>Objectives:</strong> This study identify the potential of colorimetry analysis method by using OpenCV as an alternative method for pollutant concentration measurement<strong>. Method and results:</strong> First stage, this study collecting the data of NH3 phenate and Pt-Co CU from the spectrophotometer. The first stage also was including the development of an OpenCV code. Then, the data was collected were processed to get the concentration of NH3 and Pt-Co both using OpenCV and spectrophotometer; factors that influence the Pt-Co sample image measurement process by using OpenCV-Python was analyzed too. Then in the analysis stage, the result of the two measurement method was tested by statistic determine its significant difference. The conclusion found whether OpenCV could be potential to measure the pollutant concentration or not. <strong>Conclusion:</strong> the OpenCV has potential to be use as alternative colorimetry measurement method to determine water pollutant as there is no significant difference in the spectrophotometric method results and the results from OpenCV for Pt-Co sample. Meanwhile, in this study found that the result of NH3 from spectrophotometer is nonlinear different with from OpenCV that is linear. Thus, further research is needed to test the validity of OpenCV method. The factor influence of measurement using OpenCV code is when determining the Region of Interest (ROI) and determining the pixel values for the normalized box filter

This paper presents a novel approach for improving the computation speed of the ball detection and obstacle detection processes in our robot. The conditions of obstacle detection and ball detection in our robot still have a slow processing speed, this condition makes the robot not real-time and the robot's movement is hampered. To build a good world model, things to note are obstacle information and real-time ball detection. The focus of this research is to increase the speed of the process of the ball and obstacle detection around the robot. To increase the speed of the process, it is necessary to optimize the use of the OpenCV library on the robot operating system (ROS) system to divide the process into several small processes so that the work can be optimally divided into threads that have been created. Then, minimize the use of too many frames. This information will be sent to the base station and also for obstacle avoidance purposes.

This paper presents a novel approach for improving the computation speed of the ball detection and obstacle detection processes in our robot. The conditions of obstacle detection and ball detection in our robot still have a slow processing speed, this condition makes the robot not real-time and the robot's movement is hampered. To build a good world model, things to note are obstacle information and real-time ball detection. The focus of this research is to increase the speed of the process of the ball and obstacle detection around the robot. To increase the speed of the process, it is necessary to optimize the use of the OpenCV library on the robot operating system (ROS) system to divide the process into several small processes so that the work can be optimally divided into threads that have been created. Then, minimize the use of too many frames. This information will be sent to the base station and also for obstacle avoidance purposes.

<em>The concern toward health regulations has been on high alert since the Covid pandemic. Almost all public places necessitate a thermal checkup at the entrances. This research aims to create an economic and reliant camera ecosystem, which is automated and integrated to detect individuals by their pre-recorded facial biometrics, check their heat signature against a threshold, and log that as attendance in a database with the hash of said individual. The present systems are either too expensive or unreliable if it&#39;s affordable. The research proposes a relatively inexpensive solution that is easy to implement and flexible. The most ergonomic algorithms available are selected and used to increase detection accuracy: Dlibs&#39; facial recognition model, Haar Cascade SVM, and DNN are some primary algorithms used. The most costly component of the existing systems is the thermal sensors, and the research addresses this issue using OpenCV&#39;s library methods, which are cheap to implement and use. The proposed system will work in real time with dynamic image recognition. </em>

The helmet is the main safety equipment of motorcyclists, but many drivers do not use it. The main aim of this project is to construct an automatic detection of the motorcyclist without helmet from video using OpenCV library tools. If they are not wearing the helmet, the license plate of the motorcycle is focused automatically. By using Computer Vision technique we can detect and recognize the license plate number. We make the training set of different characters of different sizes. Based on these training set, we extracted the character from images and fine is to be cut-off from the user. This billing system will recognize the character which automatically updates a database of traffic police with the details of user. Once the details are updated, motorcyclists charged for without wearing the helmet and the text sent to the database contains the details of the vehicle. When the system is damaged or not working, it will inform the police through the database system.

This study addresses the need for efficient, automated attendance systems through the design of a facial recognition application. Manual attendance systems are slow, error-prone and the retrieval of old records can be tedious. Universally assessable technological developments such as facial recognition software can easily solve these problems. However, the vast amount of computational resources required for its implementation has posed a limitation to its wide adoption. This study presents a two-step approach to resolve these challenges. By leveraging a faster, less-powerful model, as the first step, the workload of facial recognition can be distributed to save time and computational cost. A more powerful machine learning model is applied as the second step, deployed for tasks that are too complex for the first model to handle. The two-step authentication process will also reduce the occurrences of false negatives. Face_recognition, a python library is used for detection and encoding of face images read using python’s opencv library from an IP webcam. A flask application demonstrates this facial recognition functionality. The database connection and communication are accomplished using flask_sqlalchemy. A graphical user interface (web application) is used to interact with users on a high level, showing saved images of logged personnel and their times of entry. The system has a maximum accuracy of 98.78% and precision of 98.82% from tests. This shows its potential for application on a wider scale, with some added improvements such as cloud deployment and larger datasets.

Abstract: In this paper a Raspberry Pi based automated solution system focused on the real-time face monitoring of people to detect both face masks and body temperature with the help of MLX90614 sensor has been proposed. This is implemented using Python Programming with OpenCV Library, TensorFlow, Dlib Module. A security clearance system is deployed that will allow that person to enter if they are wearing a face mask and their body temperature is in check with WHO guidelines. A programmed hand sanitizer apportioning machine is mechanized, non-contact, liquor-based hand sanitizer gadget. Liquor is essentially a dissolvable, and furthermore a generally excellent sanitizer when contrasted with fluid cleanser or strong cleanser, likewise it needn't bother with water to wash off since it is unpredictable furthermore, disintegrates in a split second after application to hands. It is too demonstrated that a convergence of &gt;70% liquor can execute Covid in hands. Here, we have used IR sensor detects the hand put close to which detectsthe distance and the outcome isthe pump starts running out the hand sanitizer.Thus, the above said system will help the society by saving time and also helps in contaminating the spread of coronavirus. This can be implemented in public places such as colleges, schools, offices, shopping malls, etc. to inspect people. Keywords: Deep Learning, Open CV, Keras, Python, Tensor Flow, Computer Vision, Raspberry Pi, COVID-19, DLib, Sensor, Sanitizer, Infrared sensor.

The human face can be used to assess because of its uniqueness based on certain parameters. To perform facial recognition, the first thing that needs to be done is face detection. The author uses the Viola-Jones method to detect faces. The Viola-Jones method is known to have high speed and accuracy because it combines several concepts (Haar Features, Integral Image, AdaBoost, and Cascade Classifier) into the main method for handling objects. The principle of camera face recognition itself is that the captured face object will be processed and compared with all face images in the existing data set so that the identity of the face is known. One of the applications of face recognition is to do attendance with individual faces. The attendance process does not need physical contact interactions between humans and devices such as the fingerprint system so that during the current COVID-19 pandemic, the spread of the virus can reduce. In this research, a system that can be checked and a person's face is used as a leverage medium for arrival and return attendance using the Viola-Jones method and the LBPH algorithm. The language used is python with the OpenCV library. The PHP language is used for the user interface so that users perform attendance via a browser with the MySQL database to store attendance data. The result of the research is that using the Viola-Jones method and the LBPH algorithm faces are identified and the data is stored in the database used for data attendance. Distance and slope affect the results of face recognition. The distance is too close about 30 cm from the camera, the face cannot be detected. Instead of face position is too far approximately 200 cm, the face can still be detected but could not be identified. For a face tilt level of about 20o from perpendicular, it can still be recognized, but at a tilt degree of about 30o up or to the right, faces cannot be detected.

Abstract: In this paper a Raspberry Pi based automated solution system focused on the real-time face monitoring of people to detect both face masks and body temperature with the help of MLX90614 sensor has been proposed. This is implemented using Python Programming with OpenCV Library, TensorFlow, Dlib Module. A security clearance system is deployed that will allow that person to enter if they are wearing a face mask and their body temperature is in check with WHO guidelines. A programmed hand sanitizer apportioning machine is mechanized, non-contact, liquor-based hand sanitizer gadget. Liquor is essentially a dissolvable, and furthermore a generally excellent sanitizer when contrasted with fluid cleanser or strong cleanser, likewise it needn't bother with water to wash off since it is unpredictable furthermore, disintegrates in a split second after application to hands. It is too demonstrated that a convergence of &gt;70% liquor can execute Covid in hands. Here, we have used IR sensor detects the hand put close to it, the Arduino Uno is utilized as a microcontroller, which detects the distance and the outcome isthe pump starts running out the hand sanitizer. Thus, the above said system will help the society by saving time and also helps in contaminating the spread of coronavirus. This can be implemented in public places such as colleges, schools, offices, shopping malls, etc. to inspect people. Keywords: Deep Learning, Open CV, Keras, Python, Tensor Flow, Computer Vision, Raspberry Pi, COVID-19, DLib, Arduino, Sensor, Sanitizer, Infrared sensor

<strong>Abstract</strong> A student medical certificate validation is developed in web-based application. Based on the problem statement, medical certificate is university used to as a record that a student unable to attend to a class. It is simply too easy to obtain a sick certificate and to stay off class. Forged medical certificate has been used by students to absent their class. However, due to lack of integrity among the citizens, people tend to purchase or create forged medical certificates from various website that offers these documents. The objectives of this project are to study the optical character recognition (OCR) technology and document authentication process, and to validate the functionality of the system. The OCR technology method is applied in this project for performing an easy way to check the verification of a medical certificate. The method that have been used is for encode the information data to protect the data from being forge. Thus, the lecturer can verify the medical certificate by using web-based application to get the real information from it.

Cheminformatics is the research field that deals with information about chemical systems. This includes the chemical structure which is used in computational chemistry where quantum chemistry is too complex. The Chemistry Development Kit (CDK) was one of the first Open Science libraries in chemistry, co-founded in The Netherlands. The source code goes as far back as 1997 and has been maintained for more than 25 years. The CDK is used by many tools in drug discovery, computational toxicology, and bioinformatics. This project will develop improvements to the core library and update tools using the CDK to use the latest release.

<em>The research is aimed at finding solutions to raise awareness of librarianship students at Can Tho University of open educational resources copyright. The study was conducted using the quantitative method with 160 survey samples from the students of Library and Information Management. Quantitative data were analyzed using SPSS software. The research findings show that the librarianship students&rsquo; knowledge of open educational resources is very low. Moreover, their awareness of open educational resources copyright and their usage of this resource are still limited. This is an alarming issue for librarianship students and the university in performing copyright to this useful learning resource.</em>

Object Detection is one of the most important concepts of Computer Vision which is used in various areas like Medical Field, Security, Self Driving cars, Automated vehicle systems etc.We choose the application of Automatic License plate Recognition. Automatic License Plate Recognition is an emerging technology which is helpful in many fields and at the same time is challenging. It’s challenging because we need to get the accurate recognition of the characters in a number plate. In practical applications where sometimes the images are captured in the worst weather condition, bad lighting, wind. And to the addition, license plates are often dirty or blackened due to the smoke, half broken , or having scratches on certain characters and detection of too many license plates in a single frame. All these will act as the obstacles in developing an effective ALPR system. So basically, this is a system where recognition of characters from images using Computer Vision techniques are performed. This system is implemented in many fields like parking lots, private and public entrances, toll gates, theft control, checking the authentication of a vehicle. Procedure followed in this paper are, first capturing images from camera then loading that into system, preprocessing done using OpenCV library. Then we use Attention OCR a deep learning model to recognize the characters from an image. And later display that in the GUI and store them in the databases for different operations later.

In this era, the occurrence of vehicle theft has become a fairly frequent problem, especially in big cities like Jakarta and Surabaya. Although the technology for car security is very sophisticated (e.g. keyless system), but there are many cases that thieves still can break into the system. Once a car was stolen, the whereabouts of the car was unknown and the thief was on the loose. The goal of this research is to overcome this problem. As a device, this research works on Raspberry Pi 3 that connected with the Raspberry Pi Camera. Using the OpenCV library, with the Haar Cascade method for face detection, and Local Binary Pattern Histogram for face recognition. The device must be connected to the internet in order to send the information using a Telegram message. The research results show the success of the device system in face-recognizing between the car owner and car thief with optimal conditions in the morning until the afternoon with the light intensity around 660 to 1000 lux, and best recognizing distance at 50 cm. The success rate for obtaining the car’s location for the outdoor condition is 100%. Even if there is a slope or an error data, it can be tolerated because the difference was not too high, about 0.1-1.0 m.

ObjectiveAutomated syndromic surveillance using mobile devices is an emerging public health focus that has a high potential for enhanced disease tracking and prevention in areas with poor infrastructure. Pacific Northwest National Laboratory sought to develop an Android mobile application for syndromic biosurveillance that would i) use the phone camera to take images of human faces to detect individuals that are sick through a machine learning (ML) model and ii) collect image data to increase training data available for ML models. The initial prototype use case is for screening and tracking the health of soldiers for use by the Department of Defense’s Disease Threat Reduction Agency.IntroductionInfectious diseases present with multifarious factors requiring several efforts to detect, prevent, and break the chain of transmission. Recently, machine learning has shown to be promising for automated surveillance leading to rapid and early interventions, and extraction of phenotypic features of human faces [3, 5]. In addition, mobile devices have become a promising tool to provide on-the-ground surveillance, especially in remote areas and geolocation mapping [4].Pacific Northwest National Laboratory (PNNL) combines machine learning with mobile technology to provide a groundbreaking prototype of disease surveillance without the need for internet, just a camera. In this android application, VisionDx, a machine learning algorithm analyses human face images and within milliseconds notifies the user with confidence level whether or not the person is sick. VisionDx comes with two modes, photo and video, and additional features of history, map, and statistics. This application is the first of its kind and provides a new way to think about the future of syndromic surveillance.MethodsData. Human healthy (n = 1096) and non-healthy (n = 1269) facial images met the criteria for training the Machine Learning model after preprocessing them. The healthy images were obtained from the Chicago face database [6] and California Institute of Technology [2]. There are no known collections of disease facial images. Using open source image collection/curation services, images were identified by a variety of keywords, including specific infectious diseases. The criteria for image inclusion was 1. a frontal face was identified using OpenCV library [1], and 2. the image contained signs of disease through visual inspection (e.g., abnormal color, texture, swelling).Model. To identify a sick face from a healthy one, we used transfer machine learning and experimented with various pretrained Convolutional Neural Networks (CNN) from Google for mobile and embedded vision applications. Using MobileNet, we trained the final model with our data and deployed it to our prototype mobile app. Google Mobile Vision API and TensorFlow mobile were used to detect human faces and run predictions in the mobile app.Mobile Application. The Android app was built using Android Studio to provide an easily navigable interface that connects every action between tabbed features. The app features (i.e., Map, Camera, History, and Statistics) are in tab view format. The custom-made camera is the main feature of the app, and it contains face detection capability. A real-time health status detection function gives a level of confidence based the algorithm results found on detected faces in the camera image.ResultsPNNL's prototype Android application, VisionDx, was built with user-friendly tab views and functions to take camera images of human faces and classify them as sick or healthy through an inbuilt ML model. The major functions of the app are the camera, map, history, and statistics pages. The camera tab has a custom-made camera with face detection algorithm and classification model of sick or healthy. The camera has image or video mode and results of the algorithm are updated in milliseconds. The Statistics view provides a simple pie chart on sick/healthy images based on user selected time and location. The Map shows pins representing all labeled images stored, and the History displays all the labeled images. Clicking on an image in either view shows the image with metadata, i.e., model confidence levels, geolocation, and datetime.The CNN model prediction accuracy has ~98% validation accuracy and ~96% test accuracy. High model performance shows the possibility that deep learning could be a powerful tool to detect sickness. However, given the limited dataset, this high accuracy also means the model is most likely overfit to the data. The training set is limited: a. the number of training images is small compared to the variability in facial expressions and skin coloring, and b. the sick images only contained overt clinical signs. If trained on a larger, diverse set of data, this prototype app could prove extremely useful in surveillance efforts of individual to large groups of people in remote areas, e.g., to identify individuals in need of medical attention or get an overview of population health. In effort to improve the model, VisionDx was developed as a data collection tool to build a more comprehensive dataset. Within the tool, users can override the model prediction, i.e., false positive or false negative, with a simple toggle button. Lastly, the app was built to protect privacy so that other phone aps can't access the images unless shared by a user.ConclusionsDeveloped at PNNL for the Defense Threat Reduction Agency, VisionDx is a novel, camera-based mobile application for real-time biosurveillance and early warning in the field without internet dependency. The prototype mobile app takes pictures of faces and analyzes them using a state-of-the-art machine learning model to give two confidence levels of likelihood of being sick and healthy. With further development of a labeled dataset, such as by using the app as a data collection too, the results of the algorithm will quickly improve leading to a ground-breaking approach to public health surveillance.References1. Bradski G. (n.d.) The OpenCV Library. Retrieved Sept 30, 2018 at http://www.drdobbs.com/open-source/the-opencv-library/1844043192. Computational Vision: Archive. (1999). Retrieved Sept 22, 2018 at http://www.vision.caltech.edu/html-files/archive.html3. Ferry Q, Steinberg J, Webber C, et al (2014). Diagnostically relevant facial gestalt information from ordinary photos. ELife, 3, e02020.4. Fornace KM, Surendra H, Abidin TR, et al (2018). Use of mobile technology-based participatory mapping approaches to geolocate health facility attendees for disease surveillance in low resource settings. International Journal of Health Geographics, 17(1), 21. https://doi.org/10.1186/s12942-018-0141-05. Lopez DM, de Mello FL, G Dias, CM, et al (2017). Evaluating the Surveillance System for Spotted Fever in Brazil Using Machine-Learning Techniques. Frontiers in Public Health, 5. https://doi.org/10.3389/fpubh.2017.003236. Ma DS, Correll J, Wittenbrink B. (2015) The Chicago face database: A free stimulus set of faces and norming data. Behavior Research Methods, 47(4), 1122–1135. https://doi.org/10.3758/s13428-014-0532-5

Time- series are an important aspect of environmental modelling, and are becoming more available through the requirements of the water framework directive as well as more important with the advancement of numerical simulation techniques and increased model complexity. For this reason, within the H2020 FREEWAT project, which aims at facilitating the adoption of modeling for water resource management, the integration of a tool for time series analysis and processing has been foreseen. As a result the Observation Analysis Tool was developed to enable time-series visualisation, preprocessing of data for model development, and post-processing of model results. Observation Analysis Tool can act as a preprocessorfor calibration observations, and will be expanded to incorporate its processing capabilities directly into the calibration process. The tool consists in an expandable Python library and in an interface integrated in the QGIS FREEWAT plugin which include a large number of modelling capabilities, hydrochemical data management tools and calibration capacity. The tool has been extensively used and tested in different european institutions, to collect a number of indications to drive the future development.

Resumo O acesso aberto é um mecanismo de comunicação científica que visa democratizar o acesso a resultados de pesquisas científicas, removendo barreiras de acesso e permissão aos conteúdos publicados, barreiras essas que, em muitos casos, marginalizam autores, instituições ou países com menor capacidade de investimento financeiro. Tendo isso em vista, o objetivo deste trabalho é compreender o cenário mundial da pesquisa sobre acesso aberto, na área da Ciência da Informação, nos últimos seis anos (2015 a 2020), e identificar possíveis sugestões que possam melhorar o futuro da pesquisa nessa temática. A metodologia utilizada para alcançar o objetivo foi a análise bibliométrica, tomando como fonte de informação a base de dados Scopus, e com auxílio da ferramenta SciVal. Ao todo, foram recuperados 1.139 documentos sobre a temática de acesso aberto no período mencionado anteriormente. Das análises, foi possível caracterizar a amostra, identificar os principais colaboradores e verificar a qualidade da pesquisa sobre o tema acesso aberto. A partir dos resultados, foi possível identificar os principais atores no âmbito internacional, as áreas de pesquisa mais engajadas com o tema e propor, a partir da literatura complementar, uma proposta de impulsionamento da pesquisa em acesso aberto por meio de colaborações internacionais e nacionais entre autores.