Open Source Computer Vision Library Research Articles

Measurement of bending deformation of sheet metal using machine vision

ABSTRACT The aim of this study is to propose a fast and effective method to measure the curvature distribution and bending angle of sheet metal in the bending deformation using machine vision. An open-source computer vision library (OpenCV) was used for image processing. First, the image is calibrated for perspective and lens distortion. After that, the image is subjected to color extraction and binary conversion. The outer geometry of the material was obtained using edge detection. With the proposed method, the bending angle and curvature distribution in the four-point bending and V-bending tests are measured. The proposed machine vision method exhibits errors of 0.55% and 0.12% compared with the 3D scanner measurements for the bending angle in the four-point bend and V-bend tests after springback, respectively. The curvature distribution in bending deformation was also obtained. As an application of the proposed method, the moment – curvature diagram of the material was determined based on the obtained curvature at the center. Additionally, the optimal stroke was calculated for the target bend angle during the V-bending process. The proposed method proved to be an accurate and efficient way to measure bending deformation of sheet metal.

The development of an automated microscope image tracking and analysis system.

Microscopy image analysis plays a crucial role in understanding cellular behavior and uncovering important insights in various biological and medical research domains. Tracking cells within the time-lapse microscopy images is a fundamental technique that enables the study of cell dynamics, interactions, and migration. While manual cell tracking is possible, it is time-consuming and prone to subjective biases that impact results. In order to solve this issue, we sought to create an automated software solution, named cell analyzer, which is able to track cells within microscopy images with minimal input required from the user. The program of cell analyzer was written in Python utilizing the open source computer vision (OpenCV) library and featured a graphical user interface that makes it easy for users to access. The functions of all codes were verified through closeness, area, centroid, contrast, variance, and cell tracking test. Cell analyzer primarily utilizes image preprocessing and edge detection techniques to isolate cell boundaries for detection and analysis. It uniquely recorded the area, displacement, speed, size, and direction of detected cell objects and visualized the data collected automatically for fast analysis. Our cell analyzer provides an easy-to-use tool through a graphical user interface for tracking cell motion and analyzing quantitative cell images.

Ηand dexterities assessment in stroke patients based on augmented reality and machine learning through a box and block test

A popular and widely suggested measure for assessing unilateral hand motor skills in stroke patients is the box and block test (BBT). Our study aimed to create an augmented reality enhanced version of the BBT (AR-BBT) and evaluate its correlation to the original BBT for stroke patients. Following G-power analysis, clinical examination, and inclusion–exclusion criteria, 31 stroke patients were included in this study. AR-BBT was developed using the Open Source Computer Vision Library (OpenCV). The MediaPipe's hand tracking library uses a palm and a hand landmark machine learning model to detect and track hands. A computer and a depth camera were employed in the clinical evaluation of AR-BBT following the principles of traditional BBT. A strong correlation was achieved between the number of blocks moved in the BBT and the AR-BBT on the hemiplegic side (Pearson correlation = 0.918) and a positive statistically significant correlation (p = 0.000008). The conventional BBT is currently the preferred assessment method. However, our approach offers an advantage, as it suggests that an AR-BBT solution could remotely monitor the assessment of a home-based rehabilitation program and provide additional hand kinematic information for hand dexterities in AR environment conditions. Furthermore, it employs minimal hardware equipment.

Automating Linear and Angular Measurements for the Hip and Knee After Computed Tomography: Validation of a Three-Stage Deep Learning and Computer Vision-Based Pipeline for Pathoanatomic Assessment

BackgroundVariability in the bony morphology of pathologic hips/knees is a challenge in automating preoperative computed tomography (CT) scan measurements. With the increasing prevalence of CT for advanced preoperative planning, processing this data represents a critical bottleneck in presurgical planning, research, and development. The purpose of this study was to demonstrate a reproducible and scalable methodology for analyzing CT-based anatomy to process hip and knee anatomy for perioperative planning and execution. MethodsOne hundred patients with preoperative CT scans undergoing total knee arthroplasty for osteoarthritis were processed. A two-step deep learning pipeline of classification and segmentation models was developed that identifies landmark images and then generates contour representations. We utilized an open-source computer vision library to compute measurements. Classification models were assessed by accuracy, precision, and recall. Segmentation models were evaluated using dice and mean Intersection over Union (IOU) metrics. Contour measurements were compared against manual measurements to validate posterior condylar axis angle, sulcus angle, trochlear groove-tibial tuberosity distance, acetabular anteversion, and femoral version. ResultsClassifiers identified landmark images with accuracy of 0.91 and 0.88 for hip and knee models, respectively. Segmentation models demonstrated mean IOU scores above 0.95 with the highest dice coefficient of 0.957 [0.954-0.961] (UNet3+) and the highest mean IOU of 0.965 [0.961-0.969] (Attention U-Net). There were no statistically significant differences for the measurements taken automatically vs manually (P > 0.05). Average time for the pipeline to preprocess (48.65 +/− 4.41 sec), classify/retrieve landmark images (8.36 +/− 3.40 sec), segment images (<1 sec), and obtain measurements was 2.58 (+/− 1.92) minutes. ConclusionsA fully automated three-stage deep learning and computer vision-based pipeline of classification and segmentation models accurately localized, segmented, and measured landmark hip and knee images for patients undergoing total knee arthroplasty. Incorporation of clinical parameters, like patient-reported outcome measures and instability risk, will be important considerations alongside anatomic parameters.

Virtual Canvas using OpenCV

Abstract: The main purpose of this project is to identify the human gestures according to that specific gesturesit will perform specific tasks. Project are going to use OpenCV library, Python programming language and also we will use colour detection and image segmentation techniques to achieve this. OpenCV is an open source computer vision library for performing various advanced image processing. In colour detection we can detect any colour in a given range of HSV colour space. The image segmentation is the process of labelling every pixel in an image, where each pixel shares the same certain characteristic. Python is use for developing this whole project. After processing is complete for those input gestures it will show output on screen according to that user inputs. The main objective of the project is that the “Virtual Canvas” and put before readers some useful information regarding our project. We have made sincere attempts and have tried to present this matter in precise and compact form, the language being as simple as possible. We are sure that the information contained this volume would certainly prove useful for better insight in the scope and the dimension of this project in this true prospective. The perseverance and deep involvement shown by our group members made this vague task look very interesting and simple. We have designed this software project completely from scratch and we have not incorporated any readymade material from the Internet or any other sources to make our project seemingly more attractive and meaningful

Emergency Vehicle Detection and Traffic Prevention Using Open CV

The proposed project focuses on the development of an innovative system for "Emergency Vehicle Detection and Traffic Prevention using OpenCV." The project aims to enhance road safety and expedite emergency response by leveraging computer vision techniques implemented through the OpenCV framework. The primary objective is to design a robust algorithm that can accurately identify emergency vehicles in real-time based on distinct visual features such as color, shape, and motion patterns. OpenCV, a powerful open-source computer vision library, will serve as the foundation for image processing and analysis. Upon successful detection of an emergency vehicle, the system will employ dynamic traffic prevention measures. This includes the activation of intelligent traffic signal control to ensure the smooth and rapid passage of the emergency vehicle through intersections. The system may also interface with connected infrastructure, such as smart traffic lights, to optimize overall traffic flow and minimize delays. The project aims to contribute to public safety and emergency response efficiency by providing a reliable and automated solution for prioritizing emergency vehicles on the road. Through the integration of OpenCV, the system will offer a versatile and scalable approach to emergency vehicle detection and traffic prevention, making it a valuable contribution to the field of intelligent transportation systems. Index terms Emergency Vehicle Detection, Traffic Prevention, OpenCV, Computer Vision Techniques, Real-time Vehicle Identification, Intelligent Traffic Signal Control, Dynamic Traffic Management, Road Safety Enhancement, Emergency Response Efficiency, Public Safety, Automated Traffic Prioritization, Intelligent Transportation Systems, Visual Feature Analysis, Smart Traffic Lights Integration, Automated Traffic Flow Optimization

Recognition and Picking of Target Fruits of Crop Picking Vehicles based on Computer Vision

A crop-picking vehicle was designed to automatically pick crop fruits and reduce human labor in the later harvest. The intelligent vehicle uses a Raspberry Pi 3B + controller, combines with a monocular camera, infrared sensor, four-degree-of-freedom manipulator and manipulator, and adopts the open source computer vision library OpenCV to realize the recognition and extraction of the target fruit. Based on the actual test verification, it was concluded that the intelligent vehicle could successfully realize the picking test of simulation citrus under the condition of good ambient light.

Who Are the Anatomic Outliers Undergoing Total Knee Arthroplasty? A Computed Tomography–Based Analysis of the Hip-Knee-Ankle Axis Across 1,352 Preoperative Computed Tomographies Using a Deep Learning and Computer Vision–Based Pipeline

BackgroundDissatisfaction after total knee arthroplasty (TKA) ranges from 15 to 30%. While patient selection may be partially responsible, morphological and reconstructive challenges may be determinants. Preoperative computed tomography (CT) scans for TKA planning allow us to evaluate the hip-knee-ankle axis and establish a baseline phenotypic distribution across anatomic parameters. The purpose of this cross-sectional analysis was to establish the distributions of 27 parameters in a pre-TKA cohort and perform threshold analysis to identify anatomic outliers. MethodsThere were 1,352 pre-TKA CTs that were processed. A 2-step deep learning pipeline of classification and segmentation models identified landmark images and then generated contour representations. We used an open-source computer vision library to compute measurements for 27 anatomic metrics along the hip-knee axis. Normative distribution plots were established, and thresholds for the 15th percentile at both extremes were calculated. Metrics falling outside the central 70th percentile were considered outlier indices. A threshold analysis of outlier indices against the proportion of the cohort was performed. ResultsSignificant variation exists in pre-TKA anatomy across 27 normally distributed metrics. Threshold analysis revealed a sigmoid function with a critical point at 9 outlier indices, representing 31.2% of subjects as anatomic outliers. Metrics with the greatest variation related to deformity (tibiofemoral angle, medial proximal tibial angle, lateral distal femoral angle), bony size (tibial width, anteroposterior femoral size, femoral head size, medial femoral condyle size), intraoperative landmarks (posterior tibial slope, transepicondylar and posterior condylar axes), and neglected rotational considerations (acetabular and femoral version, femoral torsion). ConclusionsIn the largest non-industry database of pre-TKA CTs using a fully automated 3-stage deep learning and computer vision-based pipeline, marked anatomic variation exists. In the pursuit of understanding the dissatisfaction rate after TKA, acknowledging that 31% of patients represent anatomic outliers may help us better achieve anatomically personalized TKA, with or without adjunctive technology.

Automating poultry farm management with artificial intelligence: Real-time detection and tracking of broiler chickens for enhanced and efficient health monitoring.

The broiler industry plays a vital role in meeting the growing global demand for poultry meat. However, maintaining the health and well-being of broiler birds is crucial to ensure both optimal productivity and animal welfare. The increasing mortality rate of broiler chickens developed into an unavoidable issue that required attention. The major goal of this research is to monitor individual chickens for early disease identification, which will then allow for prompt isolation and treatment of sick birds, stopping the spread of pathogens and preserving the health of the flock as a whole. With an accuracy of 96%, the chosen model, YOLOv5s (You Only Look Once), performed the best. Based on their age, the algorithm was able to categorise broiler chickens. The model is converted to ONNX (Open Neural Network Exchange) format after custom training, and the centroid tracker is used for real-time tracking. After that, the output data is kept in a MySQL (My Structured Query Language) database for later use. The OpenCV (Open-Source Computer Vision Library) library is used to deploy this model on a local machine. This model seeks to identify the broiler chicken in the video frame, classify them, and maintain track of them using the tracker. Based on their age, the birds are divided into categories. Since most monitoring is required between 1 and 4weeks of age, they are divided into four age groups. The potential application of this model is in the detection of temperature, weight, flock behaviour, etc.

Real time object tracking using deep learning and OpenCV

This article summarizes the background and application fields of Open-Source Computer Vision Library (OpenCV) and deep learning and conducts research based on their object detection and tracking applications. The model algorithm selected in this article is the Convolutional Neural Networks (CNN) algorithm. CNN algorithm can be used for object detection in real-time scenarios. Moreover, the CNN algorithm has good credibility. The Python program developed based on CNN algorithm can effectively achieve real-time object tracking. The model shows good detection and tracking performance for trained targets and can be further applied in more specific scenarios in the future. Because deep learning can process large-scale data and recognize complex patterns, it can automatically learn and extract advanced features. Combining the two can achieve faster and more accurate detection and tracking of target objects. After having a large enough training sample size, it can detect and track the specified object more accurately. However, it is precisely due to the enormous sample size required for deep learning that there are still some difficulties in applying it to real-time object tracking. This article first discusses the use of supervised learning methods for deep understanding. When the input sample capacity is large enough, the machine can better reflect the real-time detection and tracking of the target object. But the time required will significantly increase. This issue still needs to be addressed in the subsequent research process.

Facial Recognition Attendance Monitoring System using Deep Learning Techniques

The Facial Recognition Attendance Monitoring System employing Deep Learning Techniques represents a cutting-edge application of artificial intelligence in educational and corporate environments. The implementation of a Facial Recognition System can aid in identifying or verifying a person's identity from a digital image. Accurate attendance records are vital to classroom evaluation. However, manual attendance tracking can result in errors, missed students, or duplicate entries. The adoption of the Face Recognition-based attendance system could help eliminate these shortcomings. This innovative approach involves utilizing a camera to capture input images, detecting faces using algorithms such as Haarcascade, Eigen values, support vector machines, or the Fisher face algorithm, verifying the faces against a database of student profiles, and marking attendance in an Excel sheet. The use of OpenCV, an open-source computer vision library, ensures the efficient

Anti-Theft System for Library

Object detection and tracking could be an immense, vivacious however inconclusive and trending area of computer vision.Due to its immense use in official surveillances, tracking modules applied in security and lots of other’s applications havemade researchers to devise a lot of optimized and specialized methods. For validation purpose live input video will be taken for the same where book will be getting detected and it can be simulated same for real-time through external hardware added. External Hardware will be having RFID integrated to detect if book is issue or not. In the end we see the proper optimized and efficient algorithm for object detection and alert for security. Object Detection is computer vision technique used to detect object and identify its localization. This technique is not only used to identify the location but also to identify which type of object it is. This CV technique is used to detect objects in real time while maintaining the level of accuracy. By bringing some advancement in it, this system can be very helpful for people to keep track of their precious things or devices which are very expensive and need to be protected. Open CV (Open-Source Computer Vision Library) is a library of programming functions mainly aimed at real- time computer vision

Facial Recognition Attendance Monitoring System using Deep Learning Techniques

The Facial Recognition Attendance Monitoring System employing Deep Learning Techniques represents a cutting-edge application of artificial intelligence in educational and corporate environments. The implementation of a Facial Recognition System can aid in identifying or verifying a person's identity from a digital image. Accurate attendance records are vital to classroom evaluation. However, manual attendance tracking can result in errors, missed students, or duplicate entries. The adoption of the Face Recognition-based attendance system could help eliminate these shortcomings. This innovative approach involves utilizing a camera to capture input images, detecting faces using algorithms such as Haarcascade, Eigen values, support vector machines, or the Fisher face algorithm, verifying the faces against a database of student profiles, and marking attendance in an Excel sheet. The use of OpenCV, an open-source computer vision library, ensures the efficient functioning of the system.

Kinematics of the Upper Eyelid and the Globe During Downward Excursion With Comparative Analysis in Patients With Thyroid Eye Disease.

To analyze the kinematics of the upper eyelid and the globe on downward excursion for potential use in monitoring thyroid eye disease (TED) progression in an objective manner. Ten normal volunteers and 10 patients with TED were studied. A high-speed (240 fps) digital camera with a coaxial light source set at a constant distance from the subjects' eyes was used to record the excursion of the upper eyelid and the globe from extreme upgaze to extreme downgaze. Clinical data, including age, gender, race, thyroid function tests, Vision, Inflammation/Congestion, Strabismus/motility restriction, Appearance/exposure score (primary surgeons' preference of TED grading system), exophthalmometry, and eyelid measurements were collected for all patients with TED. Frame-by-frame analyses of the videos were performed using Python software (version 3.6) and the Open Source Computer Vision Library. Temporal resolution was obtained by measuring the number of frames from initiation of eyelid and globe movement from extreme upgaze (t 0 ) to extreme downgaze (t f ). Spatial resolution was obtained by measuring the number of pixels the eyelid margin and the globe traversed from t 0 to t f . The data were then plotted on a graph to calculate the velocity of the upper eyelid and the globe during downward excursion. Velocimetric calculations using high-speed photography suggests that downward excursion of the upper eyelid, and the globe occurs in 2 phases: the acceleration phase and the deceleration phase. Comparative analysis of slow-motion videography demonstrates that patients with TED were found to have attenuation in the early acceleration phase of upper eyelid excursion compared with normal subjects. In patients with TED, the difference in velocity between the eyelid and the globe occurs in the early deceleration phase. The upper eyelid normally synchronizes intimately with the globe during downward eye movement. Data from this study reveal that attenuation mostly in the early deceleration phase of eyelid movement relative to the globe accounts for the dynamic eyelid lag seen on clinical examination. Further analysis is needed to show if a quantified von Graefe sign can be used as an objective means of monitoring progression in TED.

Find Missing Person using HAAR CASCADE

Abstract: The search and rescue operations for finding missing persons present significant challenges, including vast areas to cover, limited resources, and the need for timely and accurate identification. This research paper introduces a comprehensive solution that integrates Haar cascade, face recognition, and OpenCV (Open Source Computer Vision Library) to enhance the search and rescue process. By leveraging computer vision and artificial intelligence techniques, this approach aims to improve the efficiency and accuracy of locating missing individuals.

A video processing and machine learning based method for evaluating safety-critical operator engagement in a motorway control room

In safety-critical automatic systems, safety can be compromised if operators lack engagement. Effective detection of undesirable engagement states can inform the design of interventions for enhancing engagement. However, the existing engagement measurement methods suffer from several limitations which damage their effectiveness in the work environment. A novel engagement evaluation methodology, which adopts Artificial Intelligence (AI) technologies, has been proposed. It was developed using motorway control room operators as subjects. Openpose and Open Source Computer Vision Library (OpenCV) were used to estimate the body postures of operators, then a Support Vector Machine (SVM) was utilised to build the engagement evaluation model based on discrete states of operator engagement. The average accuracy of the evaluation results reached 0.89 and the weighted average precision, recall, and F1-score were all above 0.84. This study emphasises the importance of specific data labelling when measuring typical engagement states, forming the basis for potential control room improvements. Practitioner summary: This study demonstrates an automatic, real-time, objective, and relatively unobtrusive method for measuring dynamic operator engagement states. Computer vision technologies were used to estimate body posture, then machine learning (ML) was utilised to build the engagement evaluation model. The overall evaluation shows the effectiveness of this framework. Abbreviations: AI: Artificial Intelligence; OpenCV: Open Source Computer Vision Library; SVM: Support Vector Machine; UWES: Utrecht Work Engagement Scale; ISA Engagement Scale: Intellectual, Social, Affective Engagement Scale; DSSQ: Dundee Stress State Questionnaire; SSSQ: Short Stress State Questionnaire; EEG: electroencephalography; ECG: Electrocardiography; VMOE: Video-based Measurement for Operator Engagement; CMU: Carnegie Mellon University; CNN: Convolutional Neural Network; 2D: two dimensional; ML: Machine learning.

Automated invoice data extraction using image processing

Manually processing invoices which are in the form of scanned photocopies is a time-consuming process. There is a need to automate the task of extraction of data from the invoices with a similar format. In this paper we investigate and analyse various techniques of image processing and text extraction to improve the results of the optical character recognition (OCR) engine, which is applied to extract the text from the invoice. This paper also proposes the design and implementation of a web enabled invoice processing system (IPS). The IPS consists of an annotation tool and an extraction tool. The annotation tool is used to mark the fields of interest in the invoice which are to be extracted. The extraction tool makes use of opensource computer vision library (OpenCV) algorithms to detect text. The proposed system was tested on more than 25 types of invoices with the average accuracy score lying between 85% and 95%. Finally, to provide ease of use, a web application is developed which also presents the results in a structured format. The entire system is designed so as to provide flexibility and automate the process of extracting details of interest from the invoices.

Method and analysis of color changes of facial skin after applying skin makeup

This study investigated facial skin color differences before and after makeup. Toward this goal, a photo gauge, devised with a pair of color checkers as a reference, collected face images. In addition, color calibration and a deep-learning method extracted the color values of representative areas of facial skin. The photo gauge photographed 516 Chinese females before and after applying makeup. Then, the collected images were calibrated by referencing skin color patches, and the lower cheek regions’ pixel colors were extracted using open-source computer vision libraries. Following the visible color spectrum of humans, the color values were computed in L*, a*, and b* of CIE1976L*a*b*. The results showed that the facial colors of the Chinese females changed to become brighter, less reddish, and less yellowish after applying the makeup, resulting in a paler skin tone. During the experiment, subjects were given five varieties of liquid foundation to choose one sample that best fits their skin. However, we failed to find any noticeable relationship between the individual’s facial skin color characteristics and the liquid foundation selected. In addition, 55 subjects were identified according to their makeup use frequency and skill, but their color changes did not differ from the other subjects. This study provided quantitative evidence of makeup trends in the Shanghai region in China, and the method proposes a novel approach toward remote skin color research.

YONGʻINNI VIDEOTASVIRDA RANGLI FILTRLASH BILAN INTENSIVLIK OʻZGARISHI ASOSIDA ANIQLASH

The paper reviews achievements made in the field of video analytics and computer vision, which enable automatic detection of fires, based on video data. Various algorithms have been implemented in sought for effective fire detection methods using video. As one of these, a color-based fire detection algorithm is being described. However, using only one color model for fi re detection is an inefficient approach. The paper proposes a method for estimating temporal changes in pixel intensity, which is used to retrieve information about fires from fire-like objects in a video image. This method helps to calculate the average intensity value in a sequence of shots. A special software has been developed in the Python programming language using the OpenCV (Open Source Computer Vision Library) library, and the corresponding findings have been gained in view to demonstrate the effectiveness of the proposed method.

Online attendance system based on facial recognition with face mask detection.

This paper presents an online system for recording attendance based on facial recognition incorporating facial mask detection. The main objective of this project is to develop an effective attendance system based on face recognition and face mask detection, and to provide this service online through a browser interface. This would allow any user to use this system without the need to install special software. They simply need to open the interface of this system in a browser through any terminal. Recording attendance information online allows data to be easily recorded in a centralized online database. Since faces are used as biometric signatures in this project, all users registered in the system will have their profiles loaded with their face-images samples. Initially, before face recognition can be done, the model training phase based on SVM will be carried out, mainly to develop a trained model that can perform face recognition. A set of synthetic data will also be used to train the same model so that it can perform identification for users wearing face masks. The server application is coded in Python and uses the Open-Source Computer Vision (OpenCV) library for image processing. For web interfaces and the database, PHP and MySQL are used. With the integration of Python and PHP scripting programs, the developed system will be able to perform processing on online servers, while being accessible to users through a browser from any terminal. According to the results and analysis, an accuracy of about 81.8% can be achieved based on a pre-trained model for face recognition and 80% for face mask detection.