Auswahl der wissenschaftlichen Literatur zum Thema „Camera recognition“
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Zeitschriftenartikel zum Thema "Camera recognition"
Zhao, Ruiyi, Yangshi Ge, Ye Duan und Quanhong Jiang. „Large-field Gesture Tracking and Recognition for Augmented Reality Interaction“. Journal of Physics: Conference Series 2560, Nr. 1 (01.08.2023): 012016. http://dx.doi.org/10.1088/1742-6596/2560/1/012016.
Der volle Inhalt der QuelleWANG, Chenyu, Yukinori KOBAYASHI, Takanori EMARU und 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.
Der volle Inhalt der QuelleReddy, K. Manideep. „Face Recognition for Criminal Detection“. International Journal for Research in Applied Science and Engineering Technology 10, Nr. 6 (30.06.2022): 2856–60. http://dx.doi.org/10.22214/ijraset.2022.44528.
Der volle Inhalt der QuelleChen, Zhuo, Hai Bo Wu und Sheng Ping Xia. „A Cooperative Dual-Camera System for Face Recognition and Video Monitoring“. Advanced Materials Research 998-999 (Juli 2014): 784–88. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.784.
Der volle Inhalt der QuelleTseng, Hung Li, Chao Nan Hung, Sun Yen Tan, Chiu Ching Tuan, Chi Ping Lee und Wen Tzeng Huang. „Single Camera for Multiple Vehicles License Plate Localization and Recognition on Multilane Highway“. Applied Mechanics and Materials 418 (September 2013): 120–23. http://dx.doi.org/10.4028/www.scientific.net/amm.418.120.
Der volle Inhalt der QuelleFrancisca O Nwokoma, Juliet N Odii, Ikechukwu I Ayogu und James C Ogbonna. „Camera-based OCR scene text detection issues: A review“. World Journal of Advanced Research and Reviews 12, Nr. 3 (30.12.2021): 484–89. http://dx.doi.org/10.30574/wjarr.2021.12.3.0705.
Der volle Inhalt der QuelleFan, Zhijie, Zhiwei Cao, Xin Li, Chunmei Wang, Bo Jin und 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, Nr. 1 (09.03.2023): 1–18. http://dx.doi.org/10.4018/ijisp.319304.
Der volle Inhalt der QuellePark, Yeonji, Yoojin Jeong und Chaebong Sohn. „Suspicious behavior recognition using deep learning“. Journal of Advances in Military Studies 4, Nr. 1 (30.04.2021): 43–59. http://dx.doi.org/10.37944/jams.v4i1.78.
Der volle Inhalt der QuelleAke, Kanako, Tadatoshi Ogura, Yayoi Kaneko und Gregory S. A. Rasmussen. „Automated photogrammetric method to identify individual painted dogs (Lycaon pictus)“. Zoology and Ecology 29, Nr. 2 (30.07.2019): 103–8. http://dx.doi.org/10.35513/21658005.2019.2.5.
Der volle Inhalt der QuelleRusydi, Muhammad Ilhamdi, Aulia Novira, Takayuki Nakagome, Joseph Muguro, Rio Nakajima, Waweru Njeri, Kojiro Matsushita und 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, Nr. 3 (01.05.2022): 361–73. http://dx.doi.org/10.18196/jrc.v3i3.14750.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleTadesse, Girmaw Abebe. „Human activity recognition using a wearable camera“. Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/668914.
Der volle Inhalt der QuelleLos 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.
Der volle Inhalt der QuelleSoh, Ling Min. „Recognition using tagged objects“. Thesis, University of Surrey, 2000. http://epubs.surrey.ac.uk/844110/.
Der volle Inhalt der QuelleMudduluru, Sravani. „Indian Sign Language Numbers Recognition using Intel RealSense Camera“. DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1815.
Der volle Inhalt der QuelleBellando, 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.
Der volle Inhalt der QuelleBrauer, 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.
Der volle Inhalt der QuelleHannuksela, J. (Jari). „Camera based motion estimation and recognition for human-computer interaction“. Doctoral thesis, University of Oulu, 2008. http://urn.fi/urn:isbn:9789514289781.
Der volle Inhalt der QuelleAkman, 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.
Der volle Inhalt der QuelleKurihata, Hiroyuki, Tomokazu Takahashi, Ichiro Ide, Yoshito Mekada, Hiroshi Murase, Yukimasa Tamatsu und Takayuki Miyahara. „Rainy weather recognition from in-vehicle camera images for driver assistance“. IEEE, 2005. http://hdl.handle.net/2237/6798.
Der volle Inhalt der QuelleBücher zum Thema "Camera recognition"
Iwamura, Masakazu, und Faisal Shafait, Hrsg. Camera-Based Document Analysis and Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29364-1.
Der volle Inhalt der QuelleIwamura, Masakazu, und Faisal Shafait, Hrsg. Camera-Based Document Analysis and Recognition. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05167-3.
Der volle Inhalt der QuelleJaved, Omar. Automated Multi-Camera Surveillance: Algorithms and Practice. Boston, MA: Springer Science+Business Media, LLC, 2008.
Den vollen Inhalt der Quelle findenFaisal, Shafait, und SpringerLink (Online service), Hrsg. 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.
Den vollen Inhalt der Quelle findenWang, Jiang, Zicheng Liu und Ying Wu. Human Action Recognition with Depth Cameras. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04561-0.
Der volle Inhalt der QuelleUnited States. National Aeronautics and Space Administration., Hrsg. 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.
Den vollen Inhalt der Quelle findenUnited States. National Aeronautics and Space Administration., Hrsg. 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.
Den vollen Inhalt der Quelle findenUnited States. National Aeronautics and Space Administration., Hrsg. 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.
Den vollen Inhalt der Quelle findenHooper, John R. The illustrated Camaro recognition guide. Westminster, MD: J & D Publications, 1992.
Den vollen Inhalt der Quelle findenRemondino, Fabio. TOF Range-Imaging Cameras. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Camera recognition"
Zhang, Shu, Guangqi Hou und 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.
Der volle Inhalt der QuelleFischer, Stephan, Ivica Rimac und 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.
Der volle Inhalt der QuelleSrivastava, Gaurav, Johnny Park, Avinash C. Kak, Birgi Tamersoy und 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.
Der volle Inhalt der QuelleXompero, Alessio, und 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.
Der volle Inhalt der QuelleXie, Xiaohua, Yan Gao, Wei-Shi Zheng, Jianhuang Lai und 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.
Der volle Inhalt der QuelleLiu, Yanqiong, Gang Shi, Qing Cui, Yuhong Sheng und 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.
Der volle Inhalt der QuelleKasar, Thotreingam, und 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.
Der volle Inhalt der QuelleBukhari, Syed Saqib, Faisal Shafait und 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.
Der volle Inhalt der QuelleBukhari, Syed Saqib, Faisal Shafait und 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.
Der volle Inhalt der QuelleNagy, Robert, Anders Dicker und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Camera recognition"
Rajaraman, Srinivasan, Danielle M. Caruccio, Nicholas C. Fung und Cory J. Hayes. „Fully automatic, unified stereo camera and LiDAR-camera calibration“. In Automatic Target Recognition XXXI, herausgegeben von Timothy L. Overman, Riad I. Hammoud und Abhijit Mahalanobis. SPIE, 2021. http://dx.doi.org/10.1117/12.2587806.
Der volle Inhalt der QuelleWu, Haoyu, Shaomin Xiong und 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.
Der volle Inhalt der QuellePerez-Yus, A., G. Lopez-Nicolas und 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.
Der volle Inhalt der QuelleSumida, Hiroaki, Fuji Ren, Shun Nishide und 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.
Der volle Inhalt der QuelleCastells-Rufas, David, und 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.
Der volle Inhalt der QuelleKessler, Viktor, Patrick Thiam, Mohammadreza Amirian und 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.
Der volle Inhalt der QuelleHiew, B. Y., Andrew B. J. Teoh und 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.
Der volle Inhalt der QuelleShahreza, Hatef Otroshi, Alexandre Veuthey und 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.
Der volle Inhalt der QuelleYao, Yi, Chung-Hao Chen, Besma Abidi, David Page, Andreas Koschan und 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.
Der volle Inhalt der QuelleLei, Bangjun, Shuifa Sun und Sheng Zheng. „Passive geometric camera calibration for arbitrary camera configuration“. In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, herausgegeben von Mingyue Ding, Bir Bhanu, Friedrich M. Wahl und Jonathan Roberts. SPIE, 2009. http://dx.doi.org/10.1117/12.832609.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Camera recognition"
Steves, Michelle, Brian Stanton, Mary Theofanos, Dana Chisnell und Hannah Wald. Camera Recognition. National Institute of Standards and Technology, März 2013. http://dx.doi.org/10.6028/nist.ir.7921.
Der volle Inhalt der QuelleShapovalov, Viktor B., Yevhenii B. Shapovalov, Zhanna I. Bilyk, Anna P. Megalinska und Ivan O. Muzyka. The Google Lens analyzing quality: an analysis of the possibility to use in the educational process. [б. в.], Februar 2020. http://dx.doi.org/10.31812/123456789/3754.
Der volle Inhalt der QuelleTao, Yang, Amos Mizrach, Victor Alchanatis, Nachshon Shamir und Tom Porter. Automated imaging broiler chicksexing for gender-specific and efficient production. United States Department of Agriculture, Dezember 2014. http://dx.doi.org/10.32747/2014.7594391.bard.
Der volle Inhalt der QuelleYan, Yujie, und Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, Mai 2021. http://dx.doi.org/10.17760/d20410114.
Der volle Inhalt der QuelleHall, Mark, und Neil Price. Medieval Scotland: A Future for its Past. Society of Antiquaries of Scotland, September 2012. http://dx.doi.org/10.9750/scarf.09.2012.165.
Der volle Inhalt der QuelleDalglish, Chris, und Sarah Tarlow, Hrsg. Modern Scotland: Archaeology, the Modern past and the Modern present. Society of Antiquaries of Scotland, September 2012. http://dx.doi.org/10.9750/scarf.09.2012.163.
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