Academic literature on the topic 'Computer vision-based framework'
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Journal articles on the topic "Computer vision-based framework"
Almaghout, K., and A. Klimchik. "Vision-Based Robotic Comanipulation for Deforming Cables." Nelineinaya Dinamika 18, no. 5 (2022): 0. http://dx.doi.org/10.20537/nd221213.
Full textOhta, Yuichi. "3D Image Media and Computer Vision -From CV as Robot Technology to CV as Media Technology-." Journal of Robotics and Mechatronics 9, no. 2 (April 20, 1997): 92–97. http://dx.doi.org/10.20965/jrm.1997.p0092.
Full textMorley, Terence, Tim Morris, and Martin Turner. "A Computer Vision Encyclopedia-Based Framework with Illustrative UAV Applications." Computers 10, no. 3 (March 4, 2021): 29. http://dx.doi.org/10.3390/computers10030029.
Full textSHA, Liang, Guijin WANG, Xinggang LIN, and Kongqiao WANG. "A Framework of Real Time Hand Gesture Vision Based Human-Computer Interaction." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E94-A, no. 3 (2011): 979–89. http://dx.doi.org/10.1587/transfun.e94.a.979.
Full textIvorra, Eugenio, Mario Ortega, José Catalán, Santiago Ezquerro, Luis Lledó, Nicolás Garcia-Aracil, and Mariano Alcañiz. "Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms." Sensors 18, no. 8 (July 24, 2018): 2408. http://dx.doi.org/10.3390/s18082408.
Full textAtaş, Musa. "Open Cezeri Library: A novel java based matrix and computer vision framework." Computer Applications in Engineering Education 24, no. 5 (May 17, 2016): 736–43. http://dx.doi.org/10.1002/cae.21745.
Full textSharma, Rajeev, and Jose Molineros. "Computer Vision-Based Augmented Reality for Guiding Manual Assembly." Presence: Teleoperators and Virtual Environments 6, no. 3 (June 1997): 292–317. http://dx.doi.org/10.1162/pres.1997.6.3.292.
Full textSaha, Sourav, Sahibjot Kaur, Jayanta Basak, and Priya Ranjan Sinha Mahapatra. "A Computer Vision Framework for Automated Shape Retrieval." American Journal of Advanced Computing 1, no. 1 (January 1, 2020): 1–15. http://dx.doi.org/10.15864/ajac.1108.
Full textFarahbakhsh, Ehsan, Rohitash Chandra, Hugo K. H. Olierook, Richard Scalzo, Chris Clark, Steven M. Reddy, and R. Dietmar Müller. "Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data." International Journal of Remote Sensing 41, no. 5 (October 11, 2019): 1760–87. http://dx.doi.org/10.1080/01431161.2019.1674462.
Full textZhuang, Yizhou, Weimin Chen, Tao Jin, Bin Chen, He Zhang, and Wen Zhang. "A Review of Computer Vision-Based Structural Deformation Monitoring in Field Environments." Sensors 22, no. 10 (May 16, 2022): 3789. http://dx.doi.org/10.3390/s22103789.
Full textDissertations / Theses on the topic "Computer vision-based framework"
Çelik, Turgay. "A multiresolution framework for computer vision-based autonomous navigation." Thesis, University of Warwick, 2011. http://wrap.warwick.ac.uk/36782/.
Full textBerry, David T. "A knowledge-based framework for machine vision." Thesis, Heriot-Watt University, 1987. http://hdl.handle.net/10399/1022.
Full textCaudle, Eric Weaver. "An evaluation framework for designing a night vision, computer-based trainer." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA278005.
Full textThesis advisor(s): Kishore Sengupta ; Carl R. Jones. "December 1993." Includes bibliographical references. Also available online.
Abusaleh, Sumaya. "A Novel Computer Vision-Based Framework for Supervised Classification of Energy Outbreak Phenomena." Thesis, University of Bridgeport, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10746723.
Full textToday, there is a need to implement a proper design of an adequate surveillance system that detects and categorizes explosion phenomena in order to identify the explosion risk to reduce its impact through mitigation and preparedness. This dissertation introduces state-of-the-art classification of explosion phenomena through pattern recognition techniques on color images. Consequently, we present a novel taxonomy for explosion phenomena. In particular, we demonstrate different aspects of volcanic eruptions and nuclear explosions of the proposed taxonomy that include scientific formation, real examples, existing monitoring methodologies, and their limitations. In addition, we propose a novel framework designed to categorize explosion phenomena against non-explosion phenomena. Moreover, a new dataset, Volcanic and Nuclear Explosions (VNEX), was collected. The totality of VNEX is 10, 654 samples, and it includes the following patterns: pyroclastic density currents, lava fountains, lava and tephra fallout, nuclear explosions, wildfires, fireworks, and sky clouds.
In order to achieve high reliability in the proposed explosion classification framework, we propose to employ various feature extraction approaches. Thus, we calculated the intensity levels to extract the texture features. Moreover, we utilize the YCbCr color model to calculate the amplitude features. We also employ the Radix-2 Fast Fourier Transform to compute the frequency features. Furthermore, we use the uniform local binary patterns technique to compute the histogram features. Additionally, these discriminative features were combined into a single input vector that provides valuable insight of the images, and then fed into the following classification techniques: Euclidian distance, correlation, k-nearest neighbors, one-against-one multiclass support vector machines with different kernels, and the multilayer perceptron model. Evaluation results show the design of the proposed framework is effective and robust. Furthermore, a trade-off between the computation time and the classification rate was achieved.
Fang, Bing. "A Framework for Human Body Tracking Using an Agent-based Architecture." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77135.
Full textPh. D.
Basso, Maik. "A framework for autonomous mission and guidance control of unmanned aerial vehicles based on computer vision techniques." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/179536.
Full textCumputer Vision is an area of knowledge that studies the development of artificial systems capable of detecting and developing the perception of the environment through image information or multidimensional data. Nowadays, vision systems are widely integrated into robotic systems. Visual perception and manipulation are combined in two steps "look" and then "move", generating a visual feedback control loop. In this context, there is a growing interest in using computer vision techniques in unmanned aerial vehicles (UAVs), also known as drones. These techniques are applied to position the drone in autonomous flight mode, or to perform the detection of regions for aerial surveillance or points of interest. Computer vision systems generally take three steps to the operation, which are: data acquisition in numerical form, data processing and data analysis. The data acquisition step is usually performed by cameras or proximity sensors. After data acquisition, the embedded computer performs data processing by performing algorithms with measurement techniques (variables, index and coefficients), detection (patterns, objects or area) or monitoring (people, vehicles or animals). The resulting processed data is analyzed and then converted into decision commands that serve as control inputs for the autonomous robotic system In order to integrate the visual computing systems with the different UAVs platforms, this work proposes the development of a framework for mission control and guidance of UAVs based on computer vision. The framework is responsible for managing, encoding, decoding, and interpreting commands exchanged between flight controllers and visual computing algorithms. As a case study, two algorithms were developed to provide autonomy to UAVs intended for application in precision agriculture. The first algorithm performs the calculation of a reflectance coefficient used to perform the punctual, self-regulated and efficient application of agrochemicals. The second algorithm performs the identification of crop lines to perform the guidance of the UAVs on the plantation. The performance of the proposed framework and proposed algorithms was evaluated and compared with the state of the art, obtaining satisfactory results in the implementation of embedded hardware.
Sanders, Nathaniel. "A CAMERA-BASED ENERGY RELAXATION FRAMEWORK TO MINIMIZE COLOR ARTIFACTS IN A PROJECTED DISPLAY." UKnowledge, 2007. http://uknowledge.uky.edu/gradschool_theses/431.
Full textGongbo, Liang. "Pedestrian Detection Using Basic Polyline: A Geometric Framework for Pedestrian Detection." TopSCHOLAR®, 2016. http://digitalcommons.wku.edu/theses/1582.
Full textHoke, Jaclyn Ann. "A wavelet-based framework for efficient processing of digital imagery with an application to helmet-mounted vision systems." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/6435.
Full textStrand, Mattias. "A Software Framework for Facial Modelling and Tracking." Thesis, Linköping University, Department of Electrical Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54563.
Full textThe WinCandide application, a platform for face tracking and model based coding, had become out of date and needed to be upgraded. This report is based on the work of investigating possible open source GUIs and computer vision tool kits that could replace the old ones that are unsupported. Multi platform GUIs are of special interest.
Books on the topic "Computer vision-based framework"
Panin, Giorgio. Model-based visual tracking: The OpenTL framework. Hoboken, N.J: Wiley, 2011.
Find full textPanin, Giorgio. Model-Based Visual Tracking: The OpenTL Framework. Wiley & Sons, Incorporated, John, 2011.
Find full textPanin, Giorgio. Model-Based Visual Tracking: The OpenTL Framework. Wiley & Sons, Incorporated, John, 2011.
Find full textPanin, Giorgio. Model-Based Visual Tracking: The OpenTL Framework. Wiley & Sons, Incorporated, John, 2011.
Find full textBook chapters on the topic "Computer vision-based framework"
Chaudhary, Rashmi, and Manoj Kumar. "Computer Vision-Based Framework for Anomaly Detection." In Lecture Notes in Networks and Systems, 549–56. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0666-3_45.
Full textYe, Guangqi, Jason Corso, Darius Burschka, and Gregory D. Hager. "VICs: A Modular Vision-Based HCI Framework." In Lecture Notes in Computer Science, 257–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36592-3_25.
Full textCormier, Michael, Robin Cohen, Richard Mann, Kamal Rahim, and Donglin Wang. "A Robust Vision-Based Framework for Screen Readers." In Computer Vision - ECCV 2014 Workshops, 555–69. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16199-0_39.
Full textNunes, Urbano Miguel, and Yiannis Demiris. "Entropy Minimisation Framework for Event-Based Vision Model Estimation." In Computer Vision – ECCV 2020, 161–76. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58558-7_10.
Full textGupta, Savyasachi, Dhananjai Chand, and Ilaiah Kavati. "Computer Vision based Animal Collision Avoidance Framework for Autonomous Vehicles." In Communications in Computer and Information Science, 237–48. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1103-2_21.
Full textLam, Meng Chun, Anton Satria Prabuwono, Haslina Arshad, and Chee Seng Chan. "A Real-Time Vision-Based Framework for Human-Robot Interaction." In Lecture Notes in Computer Science, 257–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25191-7_25.
Full textCrispim-Junior, Carlos Fernando, and Francois Bremond. "Uncertainty Modeling Framework for Constraint-Based Elementary Scenario Detection in Vision Systems." In Computer Vision - ECCV 2014 Workshops, 269–82. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16181-5_19.
Full textSanta Cruz, Ulices, and Yasser Shoukry. "NNLander-VeriF: A Neural Network Formal Verification Framework for Vision-Based Autonomous Aircraft Landing." In Lecture Notes in Computer Science, 213–30. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06773-0_11.
Full textChen, Xiaohong, Zhengyao Lin, Minh-Thai Trinh, and Grigore Roşu. "Towards a Trustworthy Semantics-Based Language Framework via Proof Generation." In Computer Aided Verification, 477–99. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_23.
Full textGhanshala, Tejasvi, Vikas Tripathi, and Bhaskar Pant. "An Effective Vision Based Framework for the Identification of Tuberculosis in Chest X-Ray Images." In Communications in Computer and Information Science, 36–45. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6634-9_4.
Full textConference papers on the topic "Computer vision-based framework"
Cernica, Ionut, and Nirvana Popescu. "Computer Vision Based Framework For Detecting Phishing Webpages." In 2020 19th RoEduNet Conference: Networking in Education and Research (RoEduNet). IEEE, 2020. http://dx.doi.org/10.1109/roedunet51892.2020.9324850.
Full textShahid, Aasma, Alina Tayyab, Musfira Mehmood, Rida Anum, Abdul Jalil, Ahmad Ali, Haider Ali, and Javed Ahmed. "Computer vision based intruder detection framework (CV-IDF)." In 2017 2nd International Conference on Computer and Communication Systems (ICCCS). IEEE, 2017. http://dx.doi.org/10.1109/ccoms.2017.8075263.
Full textMathews, Mary Shaji, M. Prabu, Arish Pitchai, Derin Ben Roberts, and G. Rahul. "Improved Computer Vision-based Framework for Electronic Toll Collection." In 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2022. http://dx.doi.org/10.1109/confluence52989.2022.9734219.
Full textChen, Jiujun, Gang Xiao, Fei Gao, Hongbin Zhou, and Xiaofang Ying. "Vision-Based Perceptive Framework for Fish Motion." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5364666.
Full text"A YARP-BASED ARCHITECTURAL FRAMEWORK FOR ROBOTIC VISION APPLICATIONS." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0001773600650068.
Full textYang, Zixuan, Huaiyuan Teng, Jeremy Goldhawk, Ilya Kovalenko, Efe C. Balta, Felipe Lopez, Dawn Tilbury, and Kira Barton. "A Vision-Based Framework for Enhanced Quality Control in a Smart Manufacturing System." In ASME 2019 14th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/msec2019-2966.
Full textTiwari, Rohit Kumar, and Gyanendra K. Verma. "A computer vision based framework for visual gun detection using SURF." In 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO). IEEE, 2015. http://dx.doi.org/10.1109/eesco.2015.7253863.
Full textGutierrez, Julian, Shi Dong, and David Kaeli. "Vega: A Computer Vision Processing Enhancement Framework with Graph-based Acceleration." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2020. http://dx.doi.org/10.24251/hicss.2020.818.
Full textDe, Oyndrila, Puskar Deb, Sagnik Mukherjee, Sayantan Nandy, Tamal Chakraborty, and Sourav Saha. "Computer vision based framework for digit recognition by hand gesture analysis." In 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2016. http://dx.doi.org/10.1109/iemcon.2016.7746361.
Full textZhu, Xianglei, Sen Liu, Peng Zhang, and Yihai Duan. "A Unified Framework of Intelligent Vehicle Damage Assessment based on Computer Vision Technology." In 2019 IEEE 2nd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). IEEE, 2019. http://dx.doi.org/10.1109/auteee48671.2019.9033150.
Full textReports on the topic "Computer vision-based framework"
Alhasson, Haifa F., and Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.
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