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Journal articles on the topic 'Opencv'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Shen, Sitan. "Abnormal crop warning system based on OpenMV." Advances in Engineering Technology Research 9, no. 1 (2024): 665. http://dx.doi.org/10.56028/aetr.9.1.665.2024.

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In order to realize the early warning of abnormal crops, the photographing technology and image recognition technology of openMV and openCV are comprehensively applied to study the early warning of abnormal crops. The design take photos using openMV hardware platform and connects to the cloud through 5G module. Then it conducts in-depth processing such as gray processing, image denoising and boundary detection on the photos through the network server to obtain the location and size of abnormal areas, so as to help spray pesticides later and improve production efficiency.

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Domínguez, César, Jónathan Heras, and Vico Pascual. "IJ-OpenCV: Combining ImageJ and OpenCV for processing images in biomedicine." Computers in Biology and Medicine 84 (May 2017): 189–94. http://dx.doi.org/10.1016/j.compbiomed.2017.03.027.

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Song, Jaehyun, Hwanjin Jeong, and Jinkyu Jeong. "Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs." Applied Sciences 12, no. 15 (2022): 7801. http://dx.doi.org/10.3390/app12157801.

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Machine-learning-based computer vision is increasingly versatile and being leveraged by a wide range of smart devices. Due to the limited performance/energy budget of computing units in smart devices, the careful implementation of computer vision algorithms is critical. In this paper, we analyze the performance bottleneck of two well-known computer vision algorithms for object tracking: object detection and optical flow in the Open-source Computer Vision library (OpenCV). Based on our in-depth analysis of their implementation, we found the current implementation fails to utilize Open Computing

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Guilherme, M. Pereira, Albertazzi G. Jr Armando, and E. M. Haertel Maryah. "Kamera kalibráció OpenCV használatával." Fiatal Műszakiak Tudományos Ülésszaka 1. (2014) (2014): 329–32. http://dx.doi.org/10.36243/fmtu-2014.075.

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Singh, Mr Devanshu. "Virtual Mouse using OpenCV." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 1055–58. http://dx.doi.org/10.22214/ijraset.2021.38160.

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Abstract: This research introduces a novel method for controlling mouse movement with a real-time camera. Adding more buttons or repositioning the mouse's tracking ball are two common ways. Instead, we recommend that the hardware be redesigned. Our idea is to employ a camera and computer vision technologies to manage mouse tasks (clicking and scrolling), and we demonstrate how it can do all that existing mouse devices can. This project demonstrates how to construct a mouse control system.

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Yamamoto, Yuka. "Combining LabVIEW with OpenCV." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2018 (2018): 2P2—D03. http://dx.doi.org/10.1299/jsmermd.2018.2p2-d03.

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Aditya, P. S. R. V., P. Dinesh Sai, Purvaja Varati, and R. Rohan Singh. "Emotion Recognition using OpenCV." International Journal of Engineering and Advanced Technology 8, no. 5s (2019): 91–93. http://dx.doi.org/10.35940/ijeat.e1019.0585s19.

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The human face plays a pivotal role in identifying emotions, regardless of subject-independent features. For human-computer interaction, facial expressions form a platform for non-verbal communication. In this regard, a system which detects and analyses facial expressions, needs to be robust enough to account for human faces having multiple variability such as color, orientation, posture and so on. Our paper focuses on the technicalities which makes the system capable of addressing the variability associated with facial expressions. This is achieved using concepts of machine learning, deep lea

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Bachkar, Santosh, Shivam Bangar, Kumar Dalvi, and Piyush Anantwar. "Virtual Canvas using OpenCV." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 3505–12. http://dx.doi.org/10.22214/ijraset.2024.60589.

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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

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R J, SANJAI. "LANELINE DETECTION USING OPENCV." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03392.

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Abstract - In view of the huge computing, poor anti-interference ability of traditional detection algorithm, it does not meet the requirement of the vehicle system, for which this paper proposed a lane detection method based on OpenCV. Preprocessing image in the OpenCV environment, adopting LMedSquare(Least Median Square) idea to select the best subset combined with least squares method to picewise fitting the lane so that it realized automatic identification of lane. This algorithm is suitable for both straight and curve. Simulation shows that this algorithm has well real-time performance, ac

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Upendra, M., T. Anuradha, and L. Prakash. "Bird Repeller Using Opencv." Procedia Computer Science 252 (2025): 975–84. https://doi.org/10.1016/j.procs.2025.01.058.

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Sariati Syah, Riri Asyahira, and Rijal Hakiki. "The Utilization OpenCV to Measure the Water Pollutants Concentration." Journal of Environmental Engineering and Waste Management 6, no. 2 (2021): 90. http://dx.doi.org/10.33021/jenv.v6i2.1475.

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<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

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Zulkhaidi, Tengku Cut Al-Saidina, Eny Maria, and Yulianto Yulianto. "Pengenalan Pola Bentuk Wajah dengan OpenCV." Jurnal Rekayasa Teknologi Informasi (JURTI) 3, no. 2 (2020): 181. http://dx.doi.org/10.30872/jurti.v3i2.4033.

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Pada penelitian ini akan menggunakan module OpenCV pada bahasa pemrograman python untuk mengenali wajah sesorang yang menggunakan Haar Cascades untuk mengenali bentuk wajah dan mata. Tahapan awal menggunakan open source dari intel untuk data wajah dan mata, dipadukan dengan module cascade classifier pada openCV untuk merubah data menjadi pengenalan bentuk wajah dari titik pada wajah yang dianggap sesuai dengan data yang telah disediakan. Banyak dari beberapa sistem pendeteksian wajah menggunakan metode computer vision sebagai metode pendeteksi objek. Metode computer vision dikenal memiliki kec

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Supiyandi Supiyandi, Adinda Fita Hidayah, Luthfie Budie, Nurhalijah Berutu, and Fakhita Fahraini. "Pengenalan Gambar Dasar Menggunakan Python dan OpenCV." Jurnal Sistem Informasi dan Ilmu Komputer 2, no. 4 (2024): 52–61. http://dx.doi.org/10.59581/jusiik-widyakarya.v2i4.4200.

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OpenCV is a widely used library in the field of image processing and computer vision. Combined with Python's flexibility, OpenCV provides an extensive range of functions for efficient image processing, modification, analysis, and visualization. This paper aims to introduce the fundamental concepts of image processing using Python and OpenCV, including image reading, color conversion, edge detection, and image manipulation such as resizing and cropping. Furthermore, this study discusses basic analysis techniques like color distribution histograms and feature detection. By presenting these conce

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Krishna, Manne Vamshi, Gopu Abhishek Reddy, B. Prasanthi, and M. Sreevani. "Green Virtual Mouse Using OpenCV." International Journal of Computer Sciences and Engineering 7, no. 4 (2019): 575–80. http://dx.doi.org/10.26438/ijcse/v7i4.575580.

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D., Kavitha. "Multiple Object Recognition Using OpenCV." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 1736–47. http://dx.doi.org/10.47059/revistageintec.v11i2.1795.

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For automatic vision systems used in agriculture, the project presents object characteristics analysis using image processing techniques. In agriculture science, automatic object characteristics identification is important for monitoring vast areas of crops, and it detects signs of object characteristics as soon as it occurs on plant leaves. Image content characterization and supervised classifier type neural network are used in the proposed deciding method. Pre-processing, image segmentation, and detection are some of the image processing methods used in this form of decision making. An image

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Lee, Sang-Young. "OpenCV-based Object Tracking System." Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology 6, no. 5 (2016): 29–37. http://dx.doi.org/10.14257/ajmahs.2016.05.37.

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Pulli, Kari, Anatoly Baksheev, Kirill Kornyakov, and Victor Eruhimov. "Realtime Computer Vision with OpenCV." Queue 10, no. 4 (2012): 40–56. http://dx.doi.org/10.1145/2181796.2206309.

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Marathe, Ashutosh. "Smart Trolley System Using OpenCV." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 1157–61. http://dx.doi.org/10.22214/ijraset.2024.65311.

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The shopping atmosphere of the malls and commercial places nowadays is confronted by problems such as long queues at checkout counters and inefficiencies in tracking purchased items. This paper introduces the “Smart Trolley System using OpenCV,” a frontier of computer vision and the IOT present in a retail. Through a unique configuration of the web cameras and IoT devices, it is now possible to track shoppers via colour bands, rejuvenating inventory management and curtailing checkout queues. This system revamps the shopping experience with a new approach of offering convenience and saving time

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Aditya Lahoty. "Traffic Light Optimization using OpenCV." International Journal for Modern Trends in Science and Technology 6, no. 12 (2020): 171–75. http://dx.doi.org/10.46501/ijmtst061233.

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Traffic Light Optimization aims to find the solution for an increased amount of unnecessary waiting time on traffic signals. Traffic Signal Optimization is the process of changing the timing parameters relative to the length of the green light for each traffic movement and the timed relationship between signalized intersections using a computer software program. Our project aims to set the timer of green light based on real-time traffic congestion i.e. number of vehicles in a particular direction of the traffic light. To work in this project, we are using the OpenCV method to detect vehicles a

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Ramesh, G., Karanam Madhavi, P. Jaya Prakash Reddy, Abhiram Pedamallu, and Raj Kumar. "Automated Opencv-Based Presentation Controller." E3S Web of Conferences 430 (2023): 01061. http://dx.doi.org/10.1051/e3sconf/202343001061.

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Présentations have proved to be an effective way of communication in today’s world. Effective présentations are essential as they express one’s idea or analysis in professional meetings. A keyboard or a remote-controlled physical device is generally used to operate the presentations. The display can be performed using hand gestures by utilizing the features provided by Computer Vision (CV). The slides in the presentation can be navigated using specific hand gestures for each operation, like navigating forward or backward in slides. This model also allows the user to virtually draw on the scree

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Subbamma, T. Venkata. "Face Recognition System using OpenCV." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 3642–46. https://doi.org/10.22214/ijraset.2025.69065.

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Abstract: Face Recognition is an important computer vision technology that enables automatic identification ofindividuals based on facial attributes. It suffers from problems like variations and facial movements. This project utilizes OpenCV-based techniques for real-time detection and recognition of faces from real video streams. The system combines Haar Cascade for face recognition and Local Binary Histogram (LBPH) for identification to offer absolute identification. The system also automates the retrieval of student academic records by correlating identified faces to the database, offering

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Mohanadevi, Mrs A., P. A. Kanish, P. Surya, and K. Vikram. "Face Emotion Detection Using OpenCV." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43024.

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Abstract Face Emotion Detection is a technology that leverages Artificial Intelligence (AI) and Computer Vision to analyze and classify human emotions based on facial expressions. This system utilizes image processing, feature extraction, and machine learning techniques to identify emotional states such as happiness, sadness, anger, surprise, fear, disgust, and neutrality By processing facial landmarks and dynamic changes in expressions, the technology enables accurate emotion recognition. he application of face emotion detection spans various fields, including healthcare, where it aids in ear

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S M, Dikshith. "AirCanvas using OpenCV and MediaPipe." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 14671–1473. https://doi.org/10.22214/ijraset.2025.66601.

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Human-Computer Interaction (HCI) has undergone significant transformations with the advent of Artificial Intelligence (AI) and Machine Learning (ML), enhancing the ways in which users engage with computing systems. This paper introduces AirCanvas, a novel hands-free digital interaction tool that leverages air gestures for intuitive and seamless computer control. The system uses advanced image processing techniques, specifically OpenCV for visual data analysis and MediaPipe for accurate hand gesture recognition, enabling users to manipulate virtual environments without physical touch. By integr

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Syahrudin, Erwin, Ema Utami, and Anggit Dwi Hartanto. "YOLOv8-Based Distance Estimation for Blind Navigation: Performance Comparison of OpenCV and Coordinate Attention Techniques." CommIT (Communication and Information Technology) Journal 19, no. 1 (2025): 45–57. https://doi.org/10.21512/commit.v19i1.11820.

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Blindness presents a significant challenge in the development of assistive technologies, particularly for navigation, as it requires accurate distance perception to enable effective mobility for the visually impaired. The research addresses this issue by evaluating and comparing the performance of the YOLOv8 model integrated with OpenCV and the Coordinate Attention Weighting (CAW) technique for distance estimation in blind navigation systems. The main research objective is to improve distance estimation accuracy without the need for additional sensors. Initially, YOLOv8 with OpenCV shows less

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Lia Farokhah. "Perbandingan Metode Deteksi Wajah Menggunakan OpenCV Haar Cascade, OpenCV Single Shot Multibox Detector (SSD) dan DLib CNN." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 3 (2021): 609–14. http://dx.doi.org/10.29207/resti.v5i3.3125.

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Comparison of methods in face detection is needed to provide recommendation of best method. This study compared three methods in face detection, namely OpenCV haar cascade, OpenCV Single Shot Multibox Detector (SSD) and Dlib CNN. Face detection is focused on five challenging conditions, namely face detection in head position obstacles, wearing face masks, lighting, background images that have a lot of noise, differences in expression. Data testing is taken randomly on google with reference to one image consisting of more than one detected face with wild condition. The results of the comparativ

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Sapkal, Snehal Satish. "SMART ATTENDANCE TRACKER USING FACE RECOGNITION." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04181.

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Abstract - This project improves attendance tracking by using face recognition instead of manual or card-based methods. It uses OpenCV and machine learning to recognize faces in real time and mark attendance automatically. This helps avoid common problems like errors, delays, and fake attendance. The system is developed using Python and OpenCV for real-time face recognition, utilizes machine learning techniques for accurate identification, and stores attendance data in a MySQL database to manage records efficiently. Keyword’s: FaceRecognition, Attendance Tracking, Real-Time Monitoring, OpenCV,

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Tambunan, Lowis, Joel Arie Putranta Ginting, and Jonathan Martua Gultom. "Implementation of Deep Learning in the Application of Lecture Attendance System with Face Recognition Technology Based on OpenCV." Jurnal Ar Ro'is Mandalika (Armada) 2, no. 2 (2024): 100–109. http://dx.doi.org/10.59613/armada.v2i2.2867.

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   OpenCV, as an object detection library, is employed as the foundation in the development of a facial recognition system. This system utilizes the Haar Cascade Classifier method embedded in OpenCV for facial detection, providing an efficient approach to identifying individuals. The research is conducted using the Python programming language. The initial stages involve a literature review, followed by data collection necessary for system training. System design incorporates the implementation of the Haar Cascade Classifier method from OpenCV, along with data analysis to comp

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Kumar, Ravi, Nikita Rani, Harsh Vilas Kajale, Daljeet Kaur, Akansha Agarwal, and Sudhanshu Singhal. "REAL TIME FACE AND GENDER DETECTOR." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–9. http://dx.doi.org/10.55041/ijsrem36514.

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This research paper provides an excellent way to do this detect and detect a person's face using OpenCV, as well python which is part of in-depth learning. This report contains ways in which in-depth learning is an important part of a computer science can be used to determine faces using several libraries in OpenCV and python. This report will contain a proposed program that will assist in finding a person face in real time. This application can be used in various locations device and smartphone platforms, as well as several software applications. Keywords: Python, OpenCV, In-depth Reading, Fa

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Sejati, RR Puji Hajar, and Rodhiyah Mardhiyyah. "Deteksi Wajah Berbasis Facial Landmark Menggunakan OpenCV Dan Dlib." Jurnal Teknologi Informasi 5, no. 2 (2021): 144–48. http://dx.doi.org/10.36294/jurti.v5i2.2220.

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Computer science and information technology have advanced in a variety of sectors that were previously unachievable due to constraints such as hardware. Computer vision can be used to recognize an object using computer science. Objects can be recognized by taking or recording photos or videos and then processing them using specific tools and methodologies. The goal of the facial landmark-based face detection research using OpenCV and Dlib is to perform face detection in people so that it can be used for a variety of purposes in the future. The strategy employed in this study was the usage of f

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Akil, Ibnu. "FACE DETECTION PADA GAMBAR DENGAN MENGGUNAKAN OPENCV HAAR CASCADE." INTI Nusa Mandiri 17, no. 2 (2023): 48–54. http://dx.doi.org/10.33480/inti.v17i2.4000.

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Abstract—OpenCV has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. It has been proven by software companies, that is why the researcher will use it for face detection application with Java programming langguage. The purpose of this paper is trying to implement machine learning library OpenCV with Haarcascade algorithm to detect face from an image and to find the weaknesess of haarcascade algorithm. Haar cascade is proven still relliable to detect face. Abstrak— OpenCV memiliki l

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Balázs, Viktor, László Szilágyi, Antal Apagyi, and Timotei István Erdei. "OpenCV alapú táblafelismerő videóelemző szoftver létrehozása." Műszaki Tudományos Közlemények 9, no. 1 (2018): 39–42. http://dx.doi.org/10.33895/mtk-2018.09.05.

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Tóth, Kálmán. "Az Opencv lehetőségei a mérnöki munkában." Fiatal Műszakiak Tudományos Ülésszaka 1. (2010) (2010): 333–36. http://dx.doi.org/10.36243/fmtu-2010.79.

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Cai, Jianjun, Erxin Sun, and Zongjuan Chen. "OCR Service Platform Based on OpenCV." Journal of Physics: Conference Series 1883, no. 1 (2021): 012043. http://dx.doi.org/10.1088/1742-6596/1883/1/012043.

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Jain, Pratiksha, Neha Chopra, and Vaishali Gupta. "Automatic License Plate Recognition using OpenCV." International Journal of Computer Applications Technology and Research 3, no. 12 (2014): 756–61. http://dx.doi.org/10.7753/ijcatr0312.1001.

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Kumar, Ajay, Shivansh Chaudhary, Sonik Sangal, and Raj Dhama. "Face Detection and Recognition using OpenCV." International Journal of Computer Applications 184, no. 11 (2022): 23–32. http://dx.doi.org/10.5120/ijca2022922085.

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Sravan Kumar, G., Gardas Mythri, G. Tejaswini, Kakarla Ayesha Bhanu, and Kusa Vaishnavi. "EYE BALL CURSOR MOVEMENT USING OPENCV." YMER Digital 21, no. 05 (2022): 534–39. http://dx.doi.org/10.37896/ymer21.05/60.

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In this study, a specific human computer interaction system using eyeball movement is presented. Conventionally, computer system uses mouse as one of the data input devices. But in this system, we use eyes instead of mouse which provides a unique way of operating the computer with the help of eyeball movements. The implementation work underlying this system for pupil identification uses OPENCV library to control the cursor of the personal computer and moreover Eye Aspect Ratio technique is ascertained along with Dlib to detect the pupil. This system tracks the eye movements of the user with an

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B, Chandra, Kanaga Suba Raja S, Rohit M, and R. Sriram Vignesh. "Eyeball Movement Cursor Control Using OpenCV." ECS Transactions 107, no. 1 (2022): 10005–11. http://dx.doi.org/10.1149/10701.10005ecst.

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The upcoming generation is going to be evolved with an interaction that will be nonverbal, which is called the eye gaze and it also develops a new mode of communication for disabled people. The basic need for this kind of a system is that it can provide the assistance that a third person gives for the physically disabled people and hence by the means of tracking the eyeball movement this is made possible. The right, left, top, and bottom movements are incorporated concerning the movement of the eyeball. The advanced version of this system comes with a chai that has wheels on the ends, hence th

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K, Kiran Kumar, and Dr Mohammed Tajammul. "Monitoring Social Distancing Detection using OpenCV." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2801–3. http://dx.doi.org/10.22214/ijraset.2022.41722.

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Uranishi, Yuki. "OpenCV: Open Source Computer Vision Library." Journal of The Institute of Image Information and Television Engineers 72, no. 9 (2018): 736–39. http://dx.doi.org/10.3169/itej.72.736.

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Qiao, Yu Jing, Zhong He Liu, Da Xun Hu, and Jing Wei Xu. "Camera Calibration Method Based on OpenCV." Applied Mechanics and Materials 330 (June 2013): 517–20. http://dx.doi.org/10.4028/www.scientific.net/amm.330.517.

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A new calibration method is proposed on the basic of OpenCV camera model and existing calibration method. The method can be accomplished in three concerted steps. Firstly, standardization homography matrix is obtained through camera linear model namely normalized the elements in lower right corner of the original matrix, then the intrinsic parameters matrix and external parameter initial value of camera can be calibrated through solving the statically indeterminate equations with the least square method. Secondly, concerning lens distortion, nonlinear model is obtained, and using intersection

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Lee, MyounJae. "Production of Media Art using OpenCV." Journal of the Korea Convergence Society 7, no. 4 (2016): 173–80. http://dx.doi.org/10.15207/jkcs.2016.7.4.173.

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S.V, Viraktamath, Mukund Katti, Aditya Khatawkar, and Pavan Kulkarni. "Face Detection and Tracking using OpenCV." SIJ Transactions on Computer Networks & Communication Engineering 04, no. 03 (2016): 01–06. http://dx.doi.org/10.9756/sijcnce/v4i3/0103540102.

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Triyono, L., E. H. Pratisto, S. A. T. Bawono, F. A. Purnomo, Y. Yudhanto, and B. Raharjo. "Sign Language Translator Application Using OpenCV." IOP Conference Series: Materials Science and Engineering 333 (March 2018): 012109. http://dx.doi.org/10.1088/1757-899x/333/1/012109.

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Pulli, Kari, Anatoly Baksheev, Kirill Kornyakov, and Victor Eruhimov. "Real-time computer vision with OpenCV." Communications of the ACM 55, no. 6 (2012): 61–69. http://dx.doi.org/10.1145/2184319.2184337.

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Kim, Min-Seok, and Jeong Kim. "Obtaining Forming Limit Diagram Using OpenCV." Journal of the Korean Society for Precision Engineering 41, no. 9 (2024): 719–23. http://dx.doi.org/10.7736/jkspe.024.052.

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Gaurav Mathur, Yash Gupta, Chaitanya Manik, and Dr. Vijayasherly V. "Rock, Paper and Scissors Using Opencv." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 09 (2024): 3034–42. http://dx.doi.org/10.47392/irjaem.2024.0448.

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The game of Rock, Paper, Scissors is very simple. Each player picks one of the three objects and these rules are applied to see who has won that round: Paper wraps (beats) Rock, Scissors cut (beat) Paper, Rock blunts (beats) Scissors The challenge of the game is to guess what your opponent will choose and pick the appropriate object to beat them. People find it quite hard to pick a sequence of perfectly random choices, so any pattern that a player develops could be learned by the opponent and used to win the game. We will be implementing AI Based classic rock, paper and scissor game. We captur

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Xie, Guobo, and Wen Lu. "Image Edge Detection Based On Opencv." International Journal of Electronics and Electrical Engineering 1, no. 2 (2013): 104–6. http://dx.doi.org/10.12720/ijeee.1.2.104-106.

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Hussein, Arezu Rezgar, and Rasber Dhahir Rashid. "KurdFace Morph Dataset Creation Using OpenCV." Science Journal of University of Zakho 10, no. 4 (2022): 258–67. http://dx.doi.org/10.25271/sjuoz.2022.10.4.943.

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Automated facial recognition is rapidly being used to reliably identify the identities of individuals for a variety of applications, from automated border control to unlocking mobile phones. The attack of Morphing has presented a significant risk to the face recognition system (FRS) at automated border control. Face morphing is a technique for blending the facial images of two or more people such that the outcome looks like both of them. For example, a morphing attack may be used to get a fake passport by using a morphed image. This passport can be used by both the modified image contributors

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T. Kalai Selvi, S. Sasirekha, M. Manikandan, M. Obath Solomon, and M. Vignesh. "Virtual Mouse using OpenCV and VNC." June 2023 5, no. 2 (2023): 169–79. http://dx.doi.org/10.36548/jitdw.2023.2.007.

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Virtual Network Computing (VNC) plays a significant role in advanced remote access by allowing users to remotely control another computer or virtual machine over a network connection, and applies to real-world entities. Virtual remote control is the ability to use software with a graphical user interface to remotely operate a computer or virtual machine. One of the key advantages of controlling a virtual remote is that it allows users to interact with the remote system as if they were physically present at the remote location. This research proposes a design of virtual mouse that relies on Han

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Dhamodaran, Sasikala, Pratyush Pranjal Phukan, Mayank Singh, and Shijin Nandakumar. "Review on Computer Vision Using OpenCV." International Journal of Research Publication and Reviews 5, no. 5 (2024): 8608–21. http://dx.doi.org/10.55248/gengpi.5.0524.1345.

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