Literatura académica sobre el tema "Outdoor vision and weather"
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Artículos de revistas sobre el tema "Outdoor vision and weather"
Samo, Madiha, Jimiama Mosima Mafeni Mase y Grazziela Figueredo. "Deep Learning with Attention Mechanisms for Road Weather Detection". Sensors 23, n.º 2 (10 de enero de 2023): 798. http://dx.doi.org/10.3390/s23020798.
Texto completoKaroon, Kholud A. y Zainab N. Nemer. "A Review of Methods of Removing Haze from An Image". International Journal of Electrical and Electronics Research 10, n.º 3 (30 de septiembre de 2022): 742–46. http://dx.doi.org/10.37391/ijeer.100354.
Texto completoKim, Bong Keun y Yasushi Sumi. "Vision-Based Safety-Related Sensors in Low Visibility by Fog". Sensors 20, n.º 10 (15 de mayo de 2020): 2812. http://dx.doi.org/10.3390/s20102812.
Texto completoLiu, Wei, Yue Yang y Longsheng Wei. "Weather Recognition of Street Scene Based on Sparse Deep Neural Networks". Journal of Advanced Computational Intelligence and Intelligent Informatics 21, n.º 3 (19 de mayo de 2017): 403–8. http://dx.doi.org/10.20965/jaciii.2017.p0403.
Texto completoUhm, Taeyoung, Jeongwoo Park, Jungwoo Lee, Gideok Bae, Geonhui Ki y Youngho Choi. "Design of Multimodal Sensor Module for Outdoor Robot Surveillance System". Electronics 11, n.º 14 (15 de julio de 2022): 2214. http://dx.doi.org/10.3390/electronics11142214.
Texto completoOsorio Quero, C., D. Durini, J. Rangel-Magdaleno, J. Martinez-Carranza y R. Ramos-Garcia. "Single-Pixel Near-Infrared 3D Image Reconstruction in Outdoor Conditions". Micromachines 13, n.º 5 (20 de mayo de 2022): 795. http://dx.doi.org/10.3390/mi13050795.
Texto completoSu, Cheng, Yuan Biao Zhang, Wei Xia Luan, Zhi Xiong Wei y Rui Ming Zeng. "Single Image Defogging Algorithm Based on Sparsity". Applied Mechanics and Materials 373-375 (agosto de 2013): 558–63. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.558.
Texto completoYang, Hee-Deok. "Restoring Raindrops Using Attentive Generative Adversarial Networks". Applied Sciences 11, n.º 15 (30 de julio de 2021): 7034. http://dx.doi.org/10.3390/app11157034.
Texto completoKit Ng, Chin, Soon Nyean Cheong, Wen Wen-Jiun Yap y Yee Loo Foo. "Outdoor Illegal Parking Detection System Using Convolutional Neural Network on Raspberry Pi". International Journal of Engineering & Technology 7, n.º 3.7 (4 de julio de 2018): 17. http://dx.doi.org/10.14419/ijet.v7i3.7.16197.
Texto completoJung-San Lee, Jung-San Lee, Yun-Yi Fan Jung-San Lee, Hsin-Yu Lee Yun-Yi Fan, Gah Wee Yong Hsin-Yu Lee y Ying-Chin Chen Gah Wee Yong. "Image Dehazing Technique Based on Sky Weight Detection and Fusion Transmission". 網際網路技術學刊 23, n.º 5 (septiembre de 2022): 967–80. http://dx.doi.org/10.53106/160792642022092305005.
Texto completoTesis sobre el tema "Outdoor vision and weather"
CROCI, ALBERTO. "A novel approach to rainfall measuring: methodology, field test and business opportunity". Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2677708.
Texto completoAsmar, Daniel. "Vision-Inertial SLAM using Natural Features in Outdoor Environments". Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2843.
Texto completoThe above issues are addressed as follows. Firstly, a camera is used to recognize the environmental context (e. g. , indoor office, outdoor park) by analyzing the holistic spectral content of images of the robot's surroundings. A type of feature (e. g. , trees for a park) is then chosen for SLAM that is likely observable in the recognized setting. A novel tree detection system is introduced, which is based on perceptually organizing the content of images into quasi-vertical structures and marking those structures that intersect ground level as tree trunks. Secondly, a new tree recognition system is proposed, which is based on extracting Scale Invariant Feature Transform (SIFT) features on each tree trunk region and matching trees in feature space. Thirdly, dead-reckoning is performed via an Inertial Navigation System (INS), bounded by non-holonomic constraints. INS are insensitive to slippage and varying ground conditions. Finally, the developed Computer Vision and Inertial systems are integrated within the framework of an Extended Kalman Filter into a working Vision-INS SLAM system, named VisSLAM.
VisSLAM is tested on data collected during a real test run in an outdoor unstructured environment. Three test scenarios are proposed, ranging from semi-automatic detection, recognition, and initialization to a fully automated SLAM system. The first two scenarios are used to verify the presented inertial and Computer Vision algorithms in the context of localization, where results indicate accurate vehicle pose estimation for the majority of its journey. The final scenario evaluates the application of the proposed systems for SLAM, where results indicate successful operation for a long portion of the vehicle journey. Although the scope of this thesis is to operate in an outdoor park setting using tree trunks as landmarks, the developed techniques lend themselves to other environments using different natural objects as landmarks.
Catchpole, Jason James. "Adaptive Vision Based Scene Registration for Outdoor Augmented Reality". The University of Waikato, 2008. http://hdl.handle.net/10289/2581.
Texto completoAhmed, Maryum F. "Development of a stereo vision system for outdoor mobile robots". [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0016205.
Texto completoLin, Li-Heng. "Enhanced stereo vision SLAM for outdoor heavy machine rotation sensing". Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/25966.
Texto completoAlamgir, Nyma. "Computer vision based smoke and fire detection for outdoor environments". Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/201654/1/Nyma_Alamgir_Thesis.pdf.
Texto completoWilliams, Samuel Grant Dawson. "Real-Time Hybrid Tracking for Outdoor Augmented Reality". Thesis, University of Canterbury. Computer Science and Software Engineering, 2014. http://hdl.handle.net/10092/9188.
Texto completoSchreiber, Michael J. "Outdoor tracking using computer vision, xenon strobe illumination and retro-reflective landmarks". Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/18940.
Texto completoRosenquist, Calle y Andreas Evesson. "Visual Servoing In Semi-Structured Outdoor Environments". Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-653.
Texto completoThe field of autonomous vehicle navigation and localization is a highly active research
topic. The aim of this thesis is to evaluate the feasibility to use outdoor visual navigation in a semi-structured environment. The goal is to develop a visual navigation system for an autonomous golf ball collection vehicle operating on driving ranges.
The image feature extractors SIFT and PCA-SIFT was evaluated on an image database
consisting of images acquired from 19 outdoor locations over a period of several weeks to
allow different environmental conditions. The results from these tests show that SIFT-type
feature extractors are able to find and match image features with high accuracy. The results also show that this can be improved further by a combination of a lower nearest neighbour threshold and an outlier rejection method to allow more matches and a higher ratio of correct matches. Outliers were found and rejected by fitting the data to a homography model with the RANSAC robust estimator algorithm.
A simulator was developed to evaluate the suggested system with respect to pixel noise from illumination changes, weather and feature position accuracy as well as the distance to features, path shapes and the visual servoing target image (milestone) interval. The system was evaluated on a total of 3 paths, 40 test combinations and 137km driven. The results show that with the relatively simple visual servoing navigation system it is possible to use mono-vision as a sole sensor and navigate semi-structured outdoor environments such as driving ranges.
Linegar, Chris. "Vision-only localisation under extreme appearance change". Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:608762bd-5608-4e50-ab7b-da454dd52887.
Texto completoLibros sobre el tema "Outdoor vision and weather"
Tian, Jiandong. All Weather Robot Vision. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6429-8.
Texto completoPhilip, Steele. Whatever the weather! London: Purnell, 1988.
Buscar texto completoGaneri, Anita. Outdoor science. London: Evans Brothers, 1993.
Buscar texto completo(Firm), Outdoor Life Books, ed. The extreme weather survival manual. San Francisco, California: Weldon Owen, Inc., 2015.
Buscar texto completoSchreuder, Duco. Outdoor Lighting: Physics, Vision and Perception. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8602-1.
Texto completoservice), SpringerLink (Online, ed. Outdoor Lighting: Physics, Vision and Perception. Dordrecht: Springer Science + Business Media B.V, 2008.
Buscar texto completoWeatherwise: Practical weather lore for sailors and outdoor people. Newton Abbot: David & Charles, 1986.
Buscar texto completoReading weather: Where will you be when the storm hits? Helena, Mon: Falcon, 1998.
Buscar texto completoUnited States. National Weather Service. Vision 2005: National Weather Service strategic plan for weather, water, and climate services, 2000-2005. [Silver Spring, Md.?]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, 1999.
Buscar texto completoBrown, Tom. The tracker: The vision ; Awakening spirits. New York: One Spirit, 2003.
Buscar texto completoCapítulos de libros sobre el tema "Outdoor vision and weather"
Moodley, Jenade y Serestina Viriri. "Weather Characterization from Outdoor Scene Images". En Computer Vision and Graphics, 160–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00692-1_15.
Texto completoYu, Ye, Abhimitra Meka, Mohamed Elgharib, Hans-Peter Seidel, Christian Theobalt y William A. P. Smith. "Self-supervised Outdoor Scene Relighting". En Computer Vision – ECCV 2020, 84–101. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58542-6_6.
Texto completoCohen, Andrea, Johannes L. Schönberger, Pablo Speciale, Torsten Sattler, Jan-Michael Frahm y Marc Pollefeys. "Indoor-Outdoor 3D Reconstruction Alignment". En Computer Vision – ECCV 2016, 285–300. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46487-9_18.
Texto completoPaulescu, Marius, Eugenia Paulescu, Paul Gravila y Viorel Badescu. "Outdoor Operation of PV Systems". En Weather Modeling and Forecasting of PV Systems Operation, 271–324. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4649-0_9.
Texto completoTian, Jiandong. "Underwater Descattering from Light Field". En All Weather Robot Vision, 271–87. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6429-8_9.
Texto completoTian, Jiandong. "Applications and Future Work". En All Weather Robot Vision, 289–311. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6429-8_10.
Texto completoTian, Jiandong. "Spectral Power Distributions and Reflectance Calculations for Robot Vision". En All Weather Robot Vision, 29–53. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6429-8_2.
Texto completoTian, Jiandong. "Shadow Modeling and Detection". En All Weather Robot Vision, 77–119. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6429-8_4.
Texto completoTian, Jiandong. "Imaging Modeling and Camera Sensitivity Recovery". En All Weather Robot Vision, 55–75. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6429-8_3.
Texto completoTian, Jiandong. "Rain and Snow Removal". En All Weather Robot Vision, 189–227. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6429-8_7.
Texto completoActas de conferencias sobre el tema "Outdoor vision and weather"
Zhang, Jinsong, Kalyan Sunkavalli, Yannick Hold-Geoffroy, Sunil Hadap, Jonathan Eisenman y Jean-Francois Lalonde. "All-Weather Deep Outdoor Lighting Estimation". En 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.01040.
Texto completo"A Method of Weather Recognition based on Outdoor Images". En International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004724005100516.
Texto completoPan, Yiqun, Yan Qu y Yuming Li. "Cooling Loads Prediction of 2010 Shanghai World Expo". En ASME 2009 3rd International Conference on Energy Sustainability collocated with the Heat Transfer and InterPACK09 Conferences. ASMEDC, 2009. http://dx.doi.org/10.1115/es2009-90263.
Texto completoFederici, John F., Jianjun Ma y Lothar Moeller. "Weather Impact on Outdoor Terahertz Wireless Links". En NANOCOM' 15: ACM The Second Annual International Conference on Nanoscale Computing and Communication. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2800795.2800823.
Texto completoPentland, A., B. Bolles, S. Barnard y M. Fischler. "Outdoor Model-Based Vision". En Machine Vision. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/mv.1987.wa3.
Texto completoNarasimhan, Srinivasa G. y Shree K. Nayar. "Vision and the weather". En Photonics West 2001 - Electronic Imaging, editado por Bernice E. Rogowitz y Thrasyvoulos N. Pappas. SPIE, 2001. http://dx.doi.org/10.1117/12.429497.
Texto completoNayar, S. K. y S. G. Narasimhan. "Vision in bad weather". En Proceedings of the Seventh IEEE International Conference on Computer Vision. IEEE, 1999. http://dx.doi.org/10.1109/iccv.1999.790306.
Texto completoKawakami, Sota, Kei Okada, Naoko Nitta, Kazuaki Nakamura y Noboru Babaguchi. "Semi-Supervised Outdoor Image Generation Conditioned on Weather Signals". En 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412139.
Texto completoAnderson, Mark C., Kent L. Gee, Daniel J. Novakovich, Logan T. Mathews y Zachary T. Jones. "Comparing two weather-robust microphone configurations for outdoor measurements". En 179th Meeting of the Acoustical Society of America. ASA, 2020. http://dx.doi.org/10.1121/2.0001561.
Texto completoCampbell, NW, WPJ Mackeown, BT Thomas y T. Troscianko. "Automatic Interpretation of Outdoor Scenes." En British Machine Vision Conference 1995. British Machine Vision Association, 1995. http://dx.doi.org/10.5244/c.9.30.
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