Literatura científica selecionada sobre o tema "Camera recognition"
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Artigos de revistas sobre o assunto "Camera recognition"
Zhao, Ruiyi, Yangshi Ge, Ye Duan e Quanhong Jiang. "Large-field Gesture Tracking and Recognition for Augmented Reality Interaction". Journal of Physics: Conference Series 2560, n.º 1 (1 de agosto de 2023): 012016. http://dx.doi.org/10.1088/1742-6596/2560/1/012016.
Texto completo da fonteWANG, Chenyu, Yukinori KOBAYASHI, Takanori EMARU e Ankit RAVANKAR. "1A1-H04 Recognition of 3-D Grid Structure Recognition with Fixed Camera and RGB-D Camera". Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2015 (2015): _1A1—H04_1—_1A1—H04_4. http://dx.doi.org/10.1299/jsmermd.2015._1a1-h04_1.
Texto completo da fonteReddy, K. Manideep. "Face Recognition for Criminal Detection". International Journal for Research in Applied Science and Engineering Technology 10, n.º 6 (30 de junho de 2022): 2856–60. http://dx.doi.org/10.22214/ijraset.2022.44528.
Texto completo da fonteChen, Zhuo, Hai Bo Wu e Sheng Ping Xia. "A Cooperative Dual-Camera System for Face Recognition and Video Monitoring". Advanced Materials Research 998-999 (julho de 2014): 784–88. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.784.
Texto completo da fonteTseng, Hung Li, Chao Nan Hung, Sun Yen Tan, Chiu Ching Tuan, Chi Ping Lee e Wen Tzeng Huang. "Single Camera for Multiple Vehicles License Plate Localization and Recognition on Multilane Highway". Applied Mechanics and Materials 418 (setembro de 2013): 120–23. http://dx.doi.org/10.4028/www.scientific.net/amm.418.120.
Texto completo da fonteFrancisca O Nwokoma, Juliet N Odii, Ikechukwu I Ayogu e James C Ogbonna. "Camera-based OCR scene text detection issues: A review". World Journal of Advanced Research and Reviews 12, n.º 3 (30 de dezembro de 2021): 484–89. http://dx.doi.org/10.30574/wjarr.2021.12.3.0705.
Texto completo da fonteFan, Zhijie, Zhiwei Cao, Xin Li, Chunmei Wang, Bo Jin e Qianjin Tang. "Video Surveillance Camera Identity Recognition Method Fused With Multi-Dimensional Static and Dynamic Identification Features". International Journal of Information Security and Privacy 17, n.º 1 (9 de março de 2023): 1–18. http://dx.doi.org/10.4018/ijisp.319304.
Texto completo da fontePark, Yeonji, Yoojin Jeong e Chaebong Sohn. "Suspicious behavior recognition using deep learning". Journal of Advances in Military Studies 4, n.º 1 (30 de abril de 2021): 43–59. http://dx.doi.org/10.37944/jams.v4i1.78.
Texto completo da fonteAke, Kanako, Tadatoshi Ogura, Yayoi Kaneko e Gregory S. A. Rasmussen. "Automated photogrammetric method to identify individual painted dogs (Lycaon pictus)". Zoology and Ecology 29, n.º 2 (30 de julho de 2019): 103–8. http://dx.doi.org/10.35513/21658005.2019.2.5.
Texto completo da fonteRusydi, Muhammad Ilhamdi, Aulia Novira, Takayuki Nakagome, Joseph Muguro, Rio Nakajima, Waweru Njeri, Kojiro Matsushita e Minoru Sasaki. "Autonomous Movement Control of Coaxial Mobile Robot based on Aspect Ratio of Human Face for Public Relation Activity Using Stereo Thermal Camera". Journal of Robotics and Control (JRC) 3, n.º 3 (1 de maio de 2022): 361–73. http://dx.doi.org/10.18196/jrc.v3i3.14750.
Texto completo da fonteTeses / dissertações sobre o assunto "Camera recognition"
Johansson, Fredrik. "Recognition of Targets in Camera Networks". Thesis, Linköpings universitet, Institutionen för teknik och naturvetenskap, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-95351.
Texto completo da fonteTadesse, Girmaw Abebe. "Human activity recognition using a wearable camera". Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/668914.
Texto completo da fonteLos avances en tecnologías wearables facilitan la comprensión de actividades humanas utilizando cuando se usan videos grabados en primera persona para una amplia gama de aplicaciones. En esta tesis, proponemos características robustas de movimiento para el reconocimiento de actividades humana a partir de videos en primera persona. Las características propuestas codifican características discriminativas estimadas a partir de optical flow como magnitud, dirección y dinámica de movimiento. Además, diseñamos nuevas características de inercia virtual a partir de video, sin usar sensores inerciales, utilizando el movimiento del centroide de intensidad a través de los fotogramas. Los resultados obtenidos en múltiples bases de datos demuestran que las características inerciales basadas en centroides mejoran el rendimiento de reconocimiento en comparación con grid-based características. Además, proponemos un algoritmo multicapa que codifica las relaciones jerárquicas y temporales entre actividades. La primera capa opera en grupos de características que codifican eficazmente las dinámicas del movimiento y las variaciones temporales de características de apariencia entre múltiples fotogramas utilizando una jerarquía. La segunda capa aprovecha el contexto temporal ponderando las salidas de la jerarquía durante el modelado. Además, diseñamos una técnica de postprocesado para filtrar las decisiones utilizando estimaciones pasadas y la confianza de la estimación actual. Validamos el algoritmo propuesto utilizando varios clasificadores. El modelado temporal muestra una mejora del rendimiento en el reconocimiento de actividades. También investigamos el uso de redes profundas (deep networks) para simplificar el diseño manual de características a partir de videos en primera persona. Proponemos apilar espectrogramas para representar movimientos globales a corto plazo. Estos espectrogramas contienen una representación espaciotemporal de múltiples componentes de movimiento. Esto nos permite aplicar convoluciones bidimensionales para aprender funciones de movimiento. Empleamos long short-term memory recurrent networks para codificar la dependencia temporal a largo plazo entre las actividades. Además, aplicamos transferencia de conocimiento entre diferentes dominios (cross-domain knowledge) entre enfoques inerciales y basados en la visión para el reconocimiento de la actividad en primera persona. Proponemos una combinación ponderada de información de diferentes modalidades de movimiento y/o secuencias. Los resultados muestran que el algoritmo propuesto obtiene resultados competitivos en comparación con existentes algoritmos basados en deep learning, a la vez que se reduce la complejidad.
Erhard, Matthew John. "Visual intent recognition in a multiple camera environment /". Online version of thesis, 2006. http://hdl.handle.net/1850/3365.
Texto completo da fonteSoh, Ling Min. "Recognition using tagged objects". Thesis, University of Surrey, 2000. http://epubs.surrey.ac.uk/844110/.
Texto completo da fonteMudduluru, Sravani. "Indian Sign Language Numbers Recognition using Intel RealSense Camera". DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1815.
Texto completo da fonteBellando, John Louis. "Modeling and Recognition of Gestures Using a Single Camera". University of Cincinnati / OhioLINK, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=ucin973088031.
Texto completo da fonteBrauer, Henrik Siebo Peter. "Camera based human localization and recognition in smart environments". Thesis, University of the West of Scotland, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.739946.
Texto completo da fonteHannuksela, J. (Jari). "Camera based motion estimation and recognition for human-computer interaction". Doctoral thesis, University of Oulu, 2008. http://urn.fi/urn:isbn:9789514289781.
Texto completo da fonteAkman, Oytun. "Multi-camera Video Surveillance: Detection, Occlusion Handling, Tracking And Event Recognition". Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608620/index.pdf.
Texto completo da fonteKurihata, Hiroyuki, Tomokazu Takahashi, Ichiro Ide, Yoshito Mekada, Hiroshi Murase, Yukimasa Tamatsu e Takayuki Miyahara. "Rainy weather recognition from in-vehicle camera images for driver assistance". IEEE, 2005. http://hdl.handle.net/2237/6798.
Texto completo da fonteLivros sobre o assunto "Camera recognition"
Iwamura, Masakazu, e Faisal Shafait, eds. Camera-Based Document Analysis and Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29364-1.
Texto completo da fonteIwamura, Masakazu, e Faisal Shafait, eds. Camera-Based Document Analysis and Recognition. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05167-3.
Texto completo da fonteJaved, Omar. Automated Multi-Camera Surveillance: Algorithms and Practice. Boston, MA: Springer Science+Business Media, LLC, 2008.
Encontre o texto completo da fonteFaisal, Shafait, e SpringerLink (Online service), eds. Camera-Based Document Analysis and Recognition: 4th International Workshop, CBDAR 2011, Beijing, China, September 22, 2011, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Encontre o texto completo da fonteWang, Jiang, Zicheng Liu e Ying Wu. Human Action Recognition with Depth Cameras. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04561-0.
Texto completo da fonteUnited States. National Aeronautics and Space Administration., ed. A model-based approach for detection of runways and other objects in image sequences acquired using an on-board camera: Final technical report for NASA grant NAG-1-1371, "analysis of image sequences from sensors for restricted visibility operations", period of the grant January 24, 1992 to May 31, 1994. [Washington, DC: National Aeronautics and Space Administration, 1994.
Encontre o texto completo da fonteUnited States. National Aeronautics and Space Administration., ed. A model-based approach for detection of runways and other objects in image sequences acquired using an on-board camera: Final technical report for NASA grant NAG-1-1371, "analysis of image sequences from sensors for restricted visibility operations", period of the grant January 24, 1992 to May 31, 1994. [Washington, DC: National Aeronautics and Space Administration, 1994.
Encontre o texto completo da fonteUnited States. National Aeronautics and Space Administration., ed. A model-based approach for detection of runways and other objects in image sequences acquired using an on-board camera: Final technical report for NASA grant NAG-1-1371, "analysis of image sequences from sensors for restricted visibility operations", period of the grant January 24, 1992 to May 31, 1994. [Washington, DC: National Aeronautics and Space Administration, 1994.
Encontre o texto completo da fonteHooper, John R. The illustrated Camaro recognition guide. Westminster, MD: J & D Publications, 1992.
Encontre o texto completo da fonteRemondino, Fabio. TOF Range-Imaging Cameras. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Camera recognition"
Zhang, Shu, Guangqi Hou e Zhenan Sun. "Eyelash Removal Using Light Field Camera for Iris Recognition". In Biometric Recognition, 319–27. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12484-1_36.
Texto completo da fonteFischer, Stephan, Ivica Rimac e Ralf Steinmetz. "Automatic Recognition of Camera Zooms". In Visual Information and Information Systems, 253–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48762-x_32.
Texto completo da fonteSrivastava, Gaurav, Johnny Park, Avinash C. Kak, Birgi Tamersoy e J. K. Aggarwal. "Multi-camera Human Action Recognition". In Computer Vision, 501–11. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_776.
Texto completo da fonteXompero, Alessio, e Andrea Cavallaro. "Cross-Camera View-Overlap Recognition". In Lecture Notes in Computer Science, 253–69. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25075-0_19.
Texto completo da fonteXie, Xiaohua, Yan Gao, Wei-Shi Zheng, Jianhuang Lai e Junyong Zhu. "One-Snapshot Face Anti-spoofing Using a Light Field Camera". In Biometric Recognition, 108–17. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_12.
Texto completo da fonteLiu, Yanqiong, Gang Shi, Qing Cui, Yuhong Sheng e Guoqun Liu. "A Method of Personnel Location Based on Monocular Camera in Complex Terrain". In Biometric Recognition, 175–85. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97909-0_19.
Texto completo da fonteKasar, Thotreingam, e Angarai G. Ramakrishnan. "Multi-script and Multi-oriented Text Localization from Scene Images". In Camera-Based Document Analysis and Recognition, 1–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29364-1_1.
Texto completo da fonteBukhari, Syed Saqib, Faisal Shafait e Thomas M. Breuel. "Border Noise Removal of Camera-Captured Document Images Using Page Frame Detection". In Camera-Based Document Analysis and Recognition, 126–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29364-1_10.
Texto completo da fonteBukhari, Syed Saqib, Faisal Shafait e Thomas M. Breuel. "An Image Based Performance Evaluation Method for Page Dewarping Algorithms Using SIFT Features". In Camera-Based Document Analysis and Recognition, 138–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29364-1_11.
Texto completo da fonteNagy, Robert, Anders Dicker e Klaus Meyer-Wegener. "NEOCR: A Configurable Dataset for Natural Image Text Recognition". In Camera-Based Document Analysis and Recognition, 150–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29364-1_12.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Camera recognition"
Rajaraman, Srinivasan, Danielle M. Caruccio, Nicholas C. Fung e Cory J. Hayes. "Fully automatic, unified stereo camera and LiDAR-camera calibration". In Automatic Target Recognition XXXI, editado por Timothy L. Overman, Riad I. Hammoud e Abhijit Mahalanobis. SPIE, 2021. http://dx.doi.org/10.1117/12.2587806.
Texto completo da fonteWu, Haoyu, Shaomin Xiong e Toshiki Hirano. "A Real-Time Human Recognition and Tracking System With a Dual-Camera Setup". In ASME 2019 28th Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/isps2019-7469.
Texto completo da fontePerez-Yus, A., G. Lopez-Nicolas e J. J. Guerrero. "A novel hybrid camera system with depth and fisheye cameras". In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7900058.
Texto completo da fonteSumida, Hiroaki, Fuji Ren, Shun Nishide e Xin Kang. "Environment Recognition Using Robot Camera". In 2020 5th IEEE International Conference on Big Data Analytics (ICBDA). IEEE, 2020. http://dx.doi.org/10.1109/icbda49040.2020.9101205.
Texto completo da fonteCastells-Rufas, David, e Jordi Carrabina. "Camera-based Digit Recognition System". In 2006 13th IEEE International Conference on Electronics, Circuits and Systems. IEEE, 2006. http://dx.doi.org/10.1109/icecs.2006.379899.
Texto completo da fonteKessler, Viktor, Patrick Thiam, Mohammadreza Amirian e Friedhelm Schwenker. "Pain recognition with camera photoplethysmography". In 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2017. http://dx.doi.org/10.1109/ipta.2017.8310110.
Texto completo da fonteHiew, B. Y., Andrew B. J. Teoh e Y. H. Pang. "Digital camera based fingerprint recognition". In 2007 IEEE International Conference on Telecommunications and Malaysia International Conference on Communications. IEEE, 2007. http://dx.doi.org/10.1109/ictmicc.2007.4448572.
Texto completo da fonteShahreza, Hatef Otroshi, Alexandre Veuthey e Sébastien Marcel. "Face Recognition Using Lensless Camera". In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10446710.
Texto completo da fonteYao, Yi, Chung-Hao Chen, Besma Abidi, David Page, Andreas Koschan e Mongi Abidi. "Sensor planning for PTZ cameras using the probability of camera overload". In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761040.
Texto completo da fonteLei, Bangjun, Shuifa Sun e Sheng Zheng. "Passive geometric camera calibration for arbitrary camera configuration". In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, editado por Mingyue Ding, Bir Bhanu, Friedrich M. Wahl e Jonathan Roberts. SPIE, 2009. http://dx.doi.org/10.1117/12.832609.
Texto completo da fonteRelatórios de organizações sobre o assunto "Camera recognition"
Steves, Michelle, Brian Stanton, Mary Theofanos, Dana Chisnell e Hannah Wald. Camera Recognition. National Institute of Standards and Technology, março de 2013. http://dx.doi.org/10.6028/nist.ir.7921.
Texto completo da fonteShapovalov, Viktor B., Yevhenii B. Shapovalov, Zhanna I. Bilyk, Anna P. Megalinska e Ivan O. Muzyka. The Google Lens analyzing quality: an analysis of the possibility to use in the educational process. [б. в.], fevereiro de 2020. http://dx.doi.org/10.31812/123456789/3754.
Texto completo da fonteTao, Yang, Amos Mizrach, Victor Alchanatis, Nachshon Shamir e Tom Porter. Automated imaging broiler chicksexing for gender-specific and efficient production. United States Department of Agriculture, dezembro de 2014. http://dx.doi.org/10.32747/2014.7594391.bard.
Texto completo da fonteYan, Yujie, e Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, maio de 2021. http://dx.doi.org/10.17760/d20410114.
Texto completo da fonteHall, Mark, e Neil Price. Medieval Scotland: A Future for its Past. Society of Antiquaries of Scotland, setembro de 2012. http://dx.doi.org/10.9750/scarf.09.2012.165.
Texto completo da fonteDalglish, Chris, e Sarah Tarlow, eds. Modern Scotland: Archaeology, the Modern past and the Modern present. Society of Antiquaries of Scotland, setembro de 2012. http://dx.doi.org/10.9750/scarf.09.2012.163.
Texto completo da fonte