Relevant bibliographies by topics / Opencv

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Opencv'

Author: Grafiati
Published: 4 June 2021
Last updated: 24 January 2023

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Journal articles on the topic "Opencv"

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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 (August 3, 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 Language (OpenCL) accelerators (e.g., GPUs). Based on the analysis, we propose several optimization strategies and apply them to the OpenCL implementation of object tracking algorithms. Our evaluation results demonstrate the performance of the object detection is improved by up to 86% and the performance of the optical flow by up to 10%. We believe our optimization strategies can be applied to other computer vision algorithms implemented in OpenCL.
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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 (December 31, 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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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 (October 4, 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:</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
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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 (April 30, 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 (June 5, 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 data will be rearranged and, if necessary, a region of interest will be selected during preparation. For network training and classification, colour and texture features are extracted from an input. Colour characteristics such as mean and variance in the HSV colour space, as well as texture characteristics such as energy, contrast, homogeneity, and correlation. The device will be trained to automatically identify test images in order to assess object characteristics. With some training samples of that type, an automated classifier NN could be used for classification supported learning in this method. The tangent sigmoid function is used as the kernel function in this network. Finally, the simulated results show that the used network classifier has a low error rate during training and higher classification accuracy. In the previous researches Object detection has been made possible, but in our current research we have attempted to do live Object Detection using OpenCV and also the techniques involved in it.
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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 (May 31, 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 (April 2012): 40–56. http://dx.doi.org/10.1145/2181796.2206309.

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Dissertations / Theses on the topic "Opencv"

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Balasubramanian, ArunKumar. "Benchmarking of Vision-Based Prototyping and Testing Tools." Master's thesis, Universitätsbibliothek Chemnitz, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-229999.

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The demand for Advanced Driver Assistance System (ADAS) applications is increasing day by day and their development requires efficient prototyping and real time testing. ADTF (Automotive Data and Time Triggered Framework) is a software tool from Elektrobit which is used for Development, Validation and Visualization of Vision based applications, mainly for ADAS and Autonomous driving. With the help of ADTF tool, Image or Video data can be recorded and visualized and also the testing of data can be processed both on-line and off-line. The development of ADAS applications needs image and video processing and the algorithm has to be highly efficient and must satisfy Real-time requirements. The main objective of this research would be to integrate OpenCV library with ADTF cross platform. OpenCV libraries provide efficient image processing algorithms which can be used with ADTF for quick benchmarking and testing. An ADTF filter framework has been developed where the OpenCV algorithms can be directly used and the testing of the framework is carried out with .DAT and image files with a modular approach. CMake is also explained in this thesis to build the system with ease of use. The ADTF filters are developed in Microsoft Visual Studio 2010 in C++ and OpenMP API are used for Parallel programming approach.
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Hasnat, Md Abul. "Detection of circular bounding box in video streams." Master's thesis, Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-206272.

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The production line of industries are getting more efficient and having very high throughput. Different kinds of machineries are being used to make the production safe, fast, precise and reliable. Robot arm is such a machine which helps the production line to be more efficient and productive. Nowadays, many manufacturing industries are using robot-arms to get a competitive edge in manufacturing and can be outfitted for multiple applications like welding, material handling, thermal spraying, painting, drilling and so on. They are widely used to increase product quality and production demand and over all, to ensure safer, faster and efficient production. It is very important to control and maintain these machines very accurately. As a simple mistake of robot arm can cause excessive destructions and bring financial losses to the industries, the robotarms must be very accurate when they are functioning in their production settings.
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Ruggeri, Eugenio. "Tracking di oggetti mediante la libreria opencv." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7864/.

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Varano, Pietro. "Elaborazioni di immagini con la libreria OpenCV." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2010. http://amslaurea.unibo.it/810/.

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Kumar, Surinder. "Lane Detection based on Contrast Analysis." Master's thesis, Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-206227.

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Computer vision and image processing systems are ubiquitous in automotive domain and manufacturing industry. Lane detection warning systems has been an elementary part of the modern automotive industry. Due to the recent progress in the computer vision and image processing methods, economical and flexible use of computer vision is now pervasive and computing with images is not just for the realm of the science, but also for the arts and social science and even for hobbyists. Image processing is a key technology in automotive industry, even now there is hardly a single manufacturing process that is thinkable without imaging. The applications of image processing and computer vision methods in embedded systems platform, is an ongoing research area since many years. OpenCV, an open-source computer vision library containing optimized algorithms and methods for designing and implementing applications based on video and image processing techniques. These method are organized in the form of modules for specific field including, user-graphic interface, machine learning, feature extraction etc [43]. Vision-based automotive application systems become an important mechanism for lane detection and warning systems to alert a driver about the road in localization of the vehicle [1]. In automotive electronic market, for lane detection problem, vision-based approaches has been designed and developed using different electronic hardware and software components including wireless sensor, camera module, Field-Programmable Gate Array (FPGA) based systems, GPU and digital signal processors (DSP) [13]. The software module consists on the top of real-time operating systems and hardware description programming language including Verilog, or VHDL. One of the most time critical task of vision based systems is to test system applications in real physical environment with wide variety of driving scenarios and validating the whole systems as per the automotive industry standards. For validating and testing the advanced driver assistance systems, there are some commercial tools available including Assist ADTF from Elektrobit, EB company [43]. In addition to the design and strict real-time requirements for advanced driver assistance systems applications based on electronic components and embedded platform, the complexity and characteristics of the implemented algorithms are two parameters that need to be taken into consideration choosing hardware and software component [13]. The development of vision-based automotive application, based on alone electronic and micro-controller is not a feasible solution approach [35] [13] and GPU based solution are attractive but has many other issues including power consumption. In this thesis project, image and video processing module is used from OpenCV library for road lane detection problems. In proposed lane detection methods, low-level image processing algorithms and methods are used to extract relevant information for lane detection problem by applying contrast analysis at pixel level intensity values. Furthermore, the work at hand presents different approaches for solving relevant partial problems in the domain of lane detection. The aim of the work is to apply contrast analysis based on low-level image processing methods to extract relevant lane model information from the grid of intensity values of pixel elements available in image frame. The approaches presented in this project work are based on contrast analysis of binary mask image frame extracted after applying range threshold. A set of points, available in an image frame, based lane feature models are used for detecting lanes on color image frame captured from video. For the performance measurement and evaluation, the proposed methods are tested on different systems setup, including Linux, Microsoft Windows, CodeBlocks, Visual Studio 2012 and Linux based Rasbian-Jessie operating systems running on Intel i3, AMD A8 APU, and embedded systems based (Raspberry Pi 2 Model B) ARM v7 processor respectively.
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Lotankar, Akshay Naresh. "Development of a smart-phone based augmented reality view application for driver assistance systems." Master's thesis, Universitätsbibliothek Chemnitz, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-229312.

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The goal of this thesis is to develop a smartphone application for augmented reality view; it is an initial attempt to realize a driver assistance functionality using just a smartphone and an external lens. Initially it depicts a brief analysis about the most feasible development technologies for mobile application development, selecting a proper lens and positioning of the smartphone in the car. Later, it discusses the strategies for real-time object detection using OpenCV; the video frames are processed using the strategies to find patterns in the videos. Different techniques like Hough-line transform, watershed, contour detection, color segmentation, color thresholding and HAAR cascades are implemented and compared in terms of real time detection of the desired objects. Then a unified algorithm is implemented for the given scenario which overcomes the challenges faced during the conceptualization phase. Finally, the results are depicted with the snapshots of the real time detection done from the smartphone and then evaluated against the vision of the application and the achieved tasks. This thesis is concluded by stating the prospects of this mobile application in the future.
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Maddison, John. "Automatisk validering av skärmgrafik med OpenCV och Tesseract." Thesis, Linköpings universitet, Programvara och system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151912.

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I dagens flygplan finns det mycket information som på ett snabbt och pålitligt sätt behöver förmedlas till piloten via instrument på flera skärmar i cockpit. Att verifiera att skärmarna visar korrekt data för olika indata är ett tidskrävande och monotont arbete. Därför undersöker Saab möjligheten att automatisera delar av arbetet. Examensarbetet undersöker genom praktiskt implementation ifall det är möjligt att automatisera bildanalysen med hjälp av programmen OpenCV och Tesseract. Resultatet visade att det går att enkelt konstruera tester för att automatiskt identifiera oönskade förändringar i den implementerade instrumentingen.
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Phothithiraphong, Thanaset. "Automated Enrichment of Global World View Information based on Car2X." Master's thesis, Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-204974.

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The purpose of this thesis is to develop the architecture to use the Car2X for observation the local traffic sign and displays it on the OpenStreetMap to provide more information of the road side to the driver. The proposed architecture of this thesis is to convert the traffic sign into the barcode and to be scanned by the barcode scanner and then wirelessly transfers the data to the web server to store the data and displays the traffic sign on the OpenStreetMap in the web browser. It uses two Raspberry Pi boards with CAN-Bus shields for transmitting the data on the CAN-Bus system in the car, a barcode scanner to scan the barcode, a GPS module to get its location, and a WiFi dongle to wirelessly send the data. The thesis also includes the camera to detect the traffic light using OpenCV and sends the GO or STOP command to the car. The results provide the OpenStreetMap with the traffic sign which helps the driver to realize the traffic sign on the road of the desired destination. However, the accuracy of GPS is not satisfied as well as the distance of the barcode scanning, therefore, this thesis suggests that includes the gps position in the barcode and uses the camera to detect the barcode for the improvement in the future.
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Tonelli, Alfredo. "Image Processing e Computer Vision con Python e OpenCV." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20390/.

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Studio di uno degli ecosistemi software oggi maggiormente utilizzati per Image Processing e Computer Vision. Lo studio affrontato parte dalle discipline di Image Processing e Computer Vision, passando per le principali soluzioni software adottate, tra le quali spiccano il linguaggio di programmazione ad alto livello Python e la libreria di Computer Vision OpenCV, per terminare con esempi pratici di base utili per capire il funzionamento delle tecnologie illustrate.
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Семенов, О. В. "Визначення розмірів об’єктів на зображенні за допомогою бібліотеки Opencv." Thesis, Чернігів, 2021. http://ir.stu.cn.ua/123456789/23095.

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Семенов, О. В. Визначення розмірів об’єктів на зображенні за допомогою бібліотеки Opencv : випускна кваліфікаційна робота : 123 "Кoмп’ютepнa iнжeнepiя" / О. В. Семенов ; керівник роботи В. В. Казимир ; НУ "Чернігівська політехніка", кафедра iнфopмaцiйниx тa кoмп’ютepниx cиcтeм. – Чернігів, 2021. – 77 с.
Об’єктом дослідження даної роботи є процес розпізнавання об’єктів на зображенні з використанням методів комп’ютерної інженерії. Метою роботи є розробка проекту по знаходженню розмірів одягу на зображенні з використанням бібліотеки OpenCV. В першому розділі було проведено теоретичні дослідження існуючих бібліотек і їх порівняння на основі особливостей практичного застосування при розпізнаванні об’єктів на зображенні. В другому розділі був проведений вибір технічних засобів для побудови системи. В третьому розділі проведено визначення розмірів на зображенні. Об’єктом є процес розпізнавання об’єктів на зображенні з використанням методів комп’ютерної інженерії. Предметом дослідження виступають теоретичні основи та прикладні підходи до реалізації процесу розпізнавання об’єктів на зображенні з використанням методів комп’ютерної інженерії.
The object of research of this work is the process of recognizing objects in the image using computer engineering methods. The purpose of the work is to develop an application to find the parameters of the garment on the image using the OpenCV library. In the first section were reviewed the theoretical studies of existing libraries and carried out their comparison on the basis of features of practical application in recognizing objects in the image. In the second section, a selection of technical means for building the system was made. In the third section was the process of determining the parameters of the garment in the image. The object is the process of recognizing objects in the image using computer engineering methods. The subject of the study is the theoretical foundations and applied approaches for implementation recognition systems for measuring the parameters in the image using computer engineering methods.
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Books on the topic "Opencv"

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Brahmbhatt, Samarth. Practical OpenCV. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-6080-6.

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Gollapudi, Sunila. Learn Computer Vision Using OpenCV. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4261-2.

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Shiqi, Yu, ed. OpenCV jiao cheng: Ji chu pian. Beijing: Beijing hang kong hang tian da xue chu ban she, 2007.

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Dawson-Howe, Kenneth. A practical introduction to computer vision with OpenCV. Chichester, West Sussex, United Kingdon: John Wiley & Sons., Inc., 2014.

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Chung, Bryan WC. Pro Processing for Images and Computer Vision with OpenCV. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2775-6.

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Rafajłowicz, Ewaryst, Wojciech Rafajłowicz, and Andrzej Rusiecki. Algorytmy przetwarzania obrazów i wstęp do pracy z biblioteką OpenCV. Wrocław: Oficyna Wydawnicza Politechniki Wrocławskiej, 2009.

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iOS application development with OpenCV 3: Create four mobile apps and explore the world through photography and computer vision. Birmingham, UK: Packt Publishing, 2016.

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Inc, ebrary, ed. OpenCV 2 computer vision application programming cookbook: Over 50 recipes to master this library of programming functions for real-time computer vision. Birmingham, U.K: Packt Open Source Pub., 2011.

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Hellmuth, Stachel, ed. Open geometry: OpenGL + advanced geometry. New York: Springer, 1999.

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Glaeser, Georg, and Hellmuth Stachel. Open Geometry: OpenGL® + Advanced Geometry. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1428-1.

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Book chapters on the topic "Opencv"

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Ramon, Manoel Carlos. "Using OpenCV." In Intel® Galileo and Intel® Galileo Gen 2, 319–400. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6838-3_7.

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Modrzyk, Nicolas. "OpenCV with Origami." In Java Image Processing Recipes, 83–173. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3465-5_2.

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Marques, Oge. "OpenCV and iOS." In Image Processing and Computer Vision in iOS, 51–58. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54032-6_6.

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Gollapudi, Sunila. "OpenCV with Python." In Learn Computer Vision Using OpenCV, 31–50. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4261-2_2.

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Blubaugh, David Allen, Steven D. Harbour, Benjamin Sears, and Michael J. Findler. "OpenCV and Perception." In Intelligent Autonomous Drones with Cognitive Deep Learning, 327–61. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-6803-2_8.

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Schmidt, Harald. "Objektsegmentierung mit OpenCV." In Raspberry Pi programmieren mit C/C++ und Bash, 719–40. München: Carl Hanser Verlag GmbH & Co. KG, 2018. http://dx.doi.org/10.3139/9783446453494.014.

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Brahmbhatt, Samarth. "CV Bling—OpenCV Inbuilt Demos." In Practical OpenCV, 13–22. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-6080-6_3.

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Brahmbhatt, Samarth. "Introduction to Computer Vision and OpenCV." In Practical OpenCV, 3–5. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-6080-6_1.

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Brahmbhatt, Samarth. "Setting up OpenCV on Your Computer." In Practical OpenCV, 7–12. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-6080-6_2.

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Cicolani, Jeff. "An Introduction to OpenCV." In Beginning Robotics with Raspberry Pi and Arduino, 297–341. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3462-4_9.

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Conference papers on the topic "Opencv"

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Gasparakis, Harris. "Heterogeneous compute in computer vision: OpenCL in OpenCV." In IS&T/SPIE Electronic Imaging, edited by Amir Said, Onur G. Guleryuz, and Robert L. Stevenson. SPIE, 2014. http://dx.doi.org/10.1117/12.2054961.

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S, Sanjay, N. Banupriya, Sathish M, and Sujay Nithish H. "Drowsiness Detection with OpenCV." In 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2021. http://dx.doi.org/10.1109/icesc51422.2021.9532758.

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S, Sumathi, D. Vijila, and M. Shastika. "Air xylophone Using OpenCV." In 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2022. http://dx.doi.org/10.1109/icses55317.2022.9914191.

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Gupta, Naman, Purushottam Sharma, Vikas Deep, and Vinod Kumar Shukla. "Automated Attendance System Using OpenCV." In 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2020. http://dx.doi.org/10.1109/icrito48877.2020.9197936.

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Meng, Cunyan, and Xinghui Zhang. "Video Encryption Based on OpenCV." In 2010 2nd International Workshop on Database Technology and Applications (DBTA). IEEE, 2010. http://dx.doi.org/10.1109/dbta.2010.5658976.

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Adusumalli, Harish, D. Kalyani, R. Krishna Sri, M. Pratapteja, and P. V. R. D. Prasada Rao. "Face Mask Detection Using OpenCV." In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). IEEE, 2021. http://dx.doi.org/10.1109/icicv50876.2021.9388375.

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Srivastava, Pratyush, Nagraj Nagraj, and Khushi Singh. "Attendance Management System Using OpenCV." In 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, 2021. http://dx.doi.org/10.1109/icac3n53548.2021.9725755.

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Asanvitha Gundala, Divya Sai, Sai Saranya Alamuri, Asmi Firdaus, and G. Kranthi Kumar. "Implementing Augmented Reality Using OpenCV." In 2022 IEEE Delhi Section Conference (DELCON). IEEE, 2022. http://dx.doi.org/10.1109/delcon54057.2022.9753233.

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Marengoni, Mauricio, and Denise Stringhini. "High Level Computer Vision Using OpenCV." In 2011 24th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T). IEEE, 2011. http://dx.doi.org/10.1109/sibgrapi-t.2011.11.

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Yuan, Xin, Ruishuang Zhu, and Li Su. "A Calibration Method Based on OpenCV." In 2011 3rd International Workshop on Intelligent Systems and Applications (ISA). IEEE, 2011. http://dx.doi.org/10.1109/isa.2011.5873428.

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Reports on the topic "Opencv"

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He, Jim, Andrew Spencer, and Eric Chu. OpenCV and TYZX : video surveillance for tracking. Office of Scientific and Technical Information (OSTI), August 2008. http://dx.doi.org/10.2172/942060.

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Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.

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Abstract:
Facial recognition technology is named one of the main trends of recent years. It’s wide range of applications, such as access control, biometrics, video surveillance and many other interactive humanmachine systems. Facial landmarks can be described as key characteristics of the human face. Commonly found landmarks are, for example, eyes, nose or mouth corners. Analyzing these key points is useful for a variety of computer vision use cases, including biometrics, face tracking, or emotion detection. Different methods produce different facial landmarks. Some methods use only basic facial landmarks, while others bring out more detail. We use 68 facial markup, which is a common format for many datasets. Cloud computing creates all the necessary conditions for the successful implementation of even the most complex tasks. We created a web application using the Django framework, Python language, OpenCv and Dlib libraries to recognize faces in the image. The purpose of our work is to create a software system for face recognition in the photo and identify wrinkles on the face. The algorithm for determining the presence and location of various types of wrinkles and determining their geometric determination on the face is programmed.
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Scott, III, and John M. Open Component Portability Infrastructure (OPENCPI). Fort Belvoir, VA: Defense Technical Information Center, November 2009. http://dx.doi.org/10.21236/ada510918.

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Kulp, James, Shepard Siegel, and John Miller. Open Component Portability Infrastructure (OPENCPI). Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada580701.

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Jin, Zheming, Kazutomo Yoshii, Hal Finkel, and Franck Cappello. Evaluation of the OpenCL AES Kernel using the Intel FPGA SDK for OpenCL. Office of Scientific and Technical Information (OSTI), April 2017. http://dx.doi.org/10.2172/1357909.

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Tam, Wai Cheong, and Walter W. Yuen. OpenSC :. Gaithersburg, MD: National Institute of Standards and Technology, September 2019. http://dx.doi.org/10.6028/nist.tn.2064.

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Zhao, Y., X. Shen, and C. Liao. OpenK: An Open Infrastructure for the Accumulation, Sharing and Reuse of High Performance Computing Knowledge. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1617288.

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Li, Ying Wai. Basic OpenMP and Profiling. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1618304.

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Moore, Shirley, Daniel Pressel, and Juan C. Chaves. Reality Check on OpenMP Implementations. Fort Belvoir, VA: Defense Technical Information Center, April 2002. http://dx.doi.org/10.21236/ada401459.

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Utke, J. OpenAD : algorithm implementation user guide. Office of Scientific and Technical Information (OSTI), May 2004. http://dx.doi.org/10.2172/834715.

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