Добірка наукової літератури з теми "Three-dimensional learning"
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Статті в журналах з теми "Three-dimensional learning"
Roth, Dan, Ming-Hsuan Yang, and Narendra Ahuja. "Learning to Recognize Three-Dimensional Objects." Neural Computation 14, no. 5 (May 1, 2002): 1071–103. http://dx.doi.org/10.1162/089976602753633394.
Повний текст джерелаFlores-Abreu, I. Nuri, T. Andrew Hurly, and Susan D. Healy. "Three-dimensional spatial learning in hummingbirds." Animal Behaviour 85, no. 3 (March 2013): 579–84. http://dx.doi.org/10.1016/j.anbehav.2012.12.019.
Повний текст джерелаZelger, P., K. Kaser, B. Rossboth, L. Velas, G. J. Schütz, and A. Jesacher. "Three-dimensional localization microscopy using deep learning." Optics Express 26, no. 25 (December 5, 2018): 33166. http://dx.doi.org/10.1364/oe.26.033166.
Повний текст джерелаGrobéty, Marie-Claude, and Françoise Schenk. "Spatial learning in a three-dimensional maze." Animal Behaviour 43, no. 6 (June 1992): 1011–20. http://dx.doi.org/10.1016/s0003-3472(06)80014-x.
Повний текст джерелаBryant, Rita. "Three-Dimensional Learning at Camp Mind's Eye." Gifted Education International 5, no. 1 (September 1987): 29–32. http://dx.doi.org/10.1177/026142948700500106.
Повний текст джерелаGothwal, Pushpa, and Sandesh Singh Shekhawa. "Three Dimensional Cube." International Journal of Engineering & Technology 7, no. 3.30 (August 24, 2018): 90. http://dx.doi.org/10.14419/ijet.v7i3.30.18207.
Повний текст джерелаGarrett, Michael, and Mark McMahon. "Computer-Generated Three-Dimensional Training Environments." International Journal of Gaming and Computer-Mediated Simulations 2, no. 3 (July 2010): 43–60. http://dx.doi.org/10.4018/jgcms.2010070103.
Повний текст джерелаYuan, Xin Lei. "Technical Analysis on Three-Dimensional Virtual Learning Community." Advanced Materials Research 557-559 (July 2012): 2029–32. http://dx.doi.org/10.4028/www.scientific.net/amr.557-559.2029.
Повний текст джерелаUmetani, Nobuyuki, and Bernd Bickel. "Learning three-dimensional flow for interactive aerodynamic design." ACM Transactions on Graphics 37, no. 4 (August 10, 2018): 1–10. http://dx.doi.org/10.1145/3197517.3201325.
Повний текст джерелаSinha, Pawan, and Tomaso Poggio. "Role of learning in three-dimensional form perception." Nature 384, no. 6608 (December 1996): 460–63. http://dx.doi.org/10.1038/384460a0.
Повний текст джерелаДисертації з теми "Three-dimensional learning"
Benveniste, David 1977. "Cognitive conflict in learning three-dimensional space station structures." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/26750.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
"September 2004."
Includes bibliographical references (p. 87-88).
(cont.) reached very high values early in the experiment and was significantly but slightly lower in FC than in GC. The target position relative to the subject's body did not affect performance, but subjects responded significantly faster when they were visually upright than when they were upside-down. Although alternative explanations cannot be ruled out, data collected and subjects' comments suggest that unlearning the GC cognitive map posed a significant challenge, and that subjects' knowledge of modules in GC, acquired earlier in the experiment, impeded their learning in FC, at least for the complex FC we used. Results of a Perspective Taking Ability test correlated weakly but significantly with TTR performance in GC, but not in FC. Other tests of spatial skills showed no significant correlation with performance. The effects of motion sickness susceptibility and of gender are also discussed. Supported by NASA Cooperative Agreement NCC 9-58 with the National Space Biomedical Research Institute.
Astronauts train on the ground in normal gravity, in replicas of the space station. Physical constraints force the configuration of these modules on the ground to be different from the configuration of the space station in flight. Based on descriptions of mishaps in human wayfinding (Jonsson 2002), it was hypothesized that the cognitive map of the space station formed from the replicas on the ground could be hard to unlearn. Could the resulting conflict with the actual configuration in flight explain why astronauts lack survey knowledge and often lose track of their orientation? Can they be trained using virtual reality to learn the correct configuration? What makes a configuration hard to learn or unlearn? We studied the ability to learn two realistic and polarized cubic modules in immersive virtual reality. Subjects (n=19) learned these modules first separately, then attached in two different configurations: first a "ground configuration" (GC), then a "flight configuration" (FC). The intrinsic visual verticals of both modules matched in GC, but not in FC, and walls at the interface between the modules were different in the two configurations. Subjects received guided tours of the modules and, through repeated trials, had to predict the location and orientation of one wall (the target), using the wall they were facing. The environment was pseudo-randomly rotated between trials. In the two module environments, subjects were set in the first module and had to place and orient the target wall in the second. The total time to respond to each trial (TTR) and the percent of correct responses (%-correct) were measured. The TTR decreased continuously with time within each virtual environment, but was significantly larger in FC than in GC. %-Correct
by David Benveniste.
S.M.
Huang, Yawen. "Cross-modality feature learning for three-dimensional brain image synthesis." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/21226/.
Повний текст джерелаRichards, Jason T. (Jason Todd) 1975. "Three-dimensional spatial learning in a virtual space station node." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/69233.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 77-78).
Astronauts find it difficult to recognize their orientation while facing any of the viewing directions in 6-ported space station node modules. Our previous experiments tested the spatial memory of human subjects in 1-G in an analogous cubic virtual environment and showed that humans are able to learn to orient when instructed to imagine different body orientations while facing in two different directions. Can subjects do the task when facing in all 6 directions? Does training help? Does spatial memory depend on the direction of remembered targets relative to the body? Does performance depend on the subject's ability to rotate himself mentally and use imagery? How long is ability retained after training? 3D spatial learning was studied in two virtual cubic chambers, in which a picture of an animal was drawn on each wall. Through trial-by-trial exposures to a virtual chamber, subjects (n=24) had to memorize the spatial relationships among the 6 pictures around them and learn to predict the direction to a specific picture if they were facing any wall in any roll orientation. After learning in one chamber, the procedure was repeated in a second. Before being tested, subjects received computer-based instructions and practice. Half of subjects were taught to remember logical picture groupings (strategy), while the remaining (control) subjects were free to do the task as they saw fit. Subjects' retention of configurational knowledge (both chambers) and spatial ability (second chamber only, without feedback) were re-tested 1, 7, and 30 days after initial training. Response time (RT) and percent correct (% correct) learning curves were measured on all four days, while configurational knowledge was tested on the last three. All subjects ultimately learned to do the task within 36 trials in either test environment, but performed faster in the second environment than in the first (especially the strategy-trained group). The strategy group showed superior % correct and RT for above/behind targets and generally better configurational knowledge. Retention of configurational knowledge and spatial ability for both groups was good over 30 days. The subjects who reported using mental imagery (n=8) had higher scores on figure rotation tests and % correct for left/right targets. Performances by the control group on the experimental tasks were significantly correlated with those on conventional tests of field independence and 2/3D figure rotation ability. Strategy training helped those who had poorer mental rotation skills, and those who could not use mental imagery. Supported by NASA Cooperative Agreement NCC9-58 with the National Space Biomedical Research Institute, USA.
by Jason T. Richards.
S.M.
Nichols, Scott A. "Improvement of the camera calibration through the use of machine learning techniques." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/anp1587/nichols%5Fthesis.pdf.
Повний текст джерелаTitle from first page of PDF file. Document formatted into pages; contains vii, 45 p.; also contains graphics. Vita. Includes bibliographical references (p. 43-44).
Fichtl, Severin Andreas Thomas-Morus. "Developmental learning of preconditions for means-end actions from 3D vision." Thesis, University of Aberdeen, 2015. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227931.
Повний текст джерелаKaplan-Rakowski, Regina. "The Effect of Stereoscopic Three-Dimensional Images on Recall of Second Language Vocabulary." OpenSIUC, 2016. https://opensiuc.lib.siu.edu/dissertations/1240.
Повний текст джерелаJärrendahl, Hannes. "Automatic Detection of Anatomical Landmarks in Three-Dimensional MRI." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-130944.
Повний текст джерелаMcManigle, John E. "Three-dimensional geometric image analysis for interventional electrophysiology." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:2f36fa8e-9c64-4807-97c0-25e63398da7e.
Повний текст джерелаSweet, Monica Ann. "Representational flexibility in the three-year-old : evidence from dimensional change tasks /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC IP addresses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3112192.
Повний текст джерелаAl-Ashqar, Rami. "Relationship descriptors for interactive motion adaptation." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/29008.
Повний текст джерелаКниги з теми "Three-dimensional learning"
(Firm), Alias/Wavefront, ed. Learning Maya 5. [Toronto]: Alias/Wavefront, 2003.
Знайти повний текст джерелаBell-Smith, Clifford John. Three dimensional awareness, childhood drawing, and the learning disabled student. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1992.
Знайти повний текст джерела(Firm), Alias, ed. Learning Maya 7. [Toronto, Ont.]: Alias, 2005.
Знайти повний текст джерелаGérard, Medioni, ed. Tensor voting: A perceptual organization approach to computer vision and machine learning. [San Rafael, Calif.]: Morgan & Claypool Publishers, 2007.
Знайти повний текст джерелаMarc-André, Guindon, ed. Learning Autodesk Maya 2008: The special effects handbook. San Rafael, Calif: Autodesk, Inc., 2007.
Знайти повний текст джерелаLearning Autodesk 3ds Max Design 2010: Essentials. San Rafael, CA: Autodesk/Focal Press, 2009.
Знайти повний текст джерелаHess, D. Roland. Blender foundations: The essential guide to learning Blender 2.5. Burlington, MA: Elsevier, 2010.
Знайти повний текст джерелаBlender foundations: The essential guide to learning Blender 2.6. Burlington, MA: Elsevier, 2010.
Знайти повний текст джерелаLearning Autodesk Maya 2009: The special effects handbook. San Rafael, CA: Autodesk, 2008.
Знайти повний текст джерелаDann, Wanda. Learning to program with Alice. Upper Saddle River, NJ: Pearson/Prentice Hall, 2007.
Знайти повний текст джерелаЧастини книг з теми "Three-dimensional learning"
Baker, Tim. "Three-dimensional Learning." In Attracting and Retaining Talent, 226–40. London: Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137411754_12.
Повний текст джерелаRen, Qiang, Yinpeng Wang, Yongzhong Li, and Shutong Qi. "Three-Dimensional Electromagnetic Scattering Solver." In Sophisticated Electromagnetic Forward Scattering Solver via Deep Learning, 99–122. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6261-4_5.
Повний текст джерелаStieff, Mike, Robert C. Bateman, and David H. Uttal. "Teaching and Learning with Three-dimensional Representations." In Visualization in Science Education, 93–120. Dordrecht: Springer Netherlands, 2005. http://dx.doi.org/10.1007/1-4020-3613-2_7.
Повний текст джерелаGao, Ying, Hongshuai Han, Fei Ge, and Shuxia Guo. "Visualization of Multi-dimensional Information of Electromagnetic Environment Based on Three Dimensional Spheres." In E-Learning and Games, 163–72. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40259-8_14.
Повний текст джерелаTeshima, Yoshinori, Yasunari Watanabe, Yohsuke Hosoya, Kazuma Sakai, Tsukasa Nakano, Akiko Tanaka, Toshiaki Aomatsu, et al. "Three-Dimensional Models of Earth for Tactile Learning." In Lecture Notes in Computer Science, 116–19. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41267-2_16.
Повний текст джерелаZhu, Yao-Lin, Xiao-Yu Wang, Tao-Ruan Wan, and Yu-Qiao Yang. "The Analysis and Creation of Mogao Caves’ Three-Dimensional Model." In E-Learning and Games, 191–98. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65849-0_20.
Повний текст джерелаPeng, Gao. "Application of Computer Three-Dimensional Learning in Hybrid Teaching." In Lecture Notes in Electrical Engineering, 2225–30. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0115-6_271.
Повний текст джерелаRichards, Robert A., and Sheri D. Sheppard. "A learning classifier system for three-dimensional shape optimization." In Parallel Problem Solving from Nature — PPSN IV, 1032–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61723-x_1066.
Повний текст джерелаBoda, István Károly, and Erzsébet Tóth. "Text-Based Second Language Learning in the Three-Dimensional Space." In Accentuated Innovations in Cognitive Info-Communication, 125–48. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10956-0_6.
Повний текст джерелаChen, Juanjuan. "Using a Three-Dimensional Cognitive Mapping Approach to Support Inquiry Learning." In Cognitive Mapping for Problem-Based and Inquiry Learning, 131–55. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003305439-15.
Повний текст джерелаТези доповідей конференцій з теми "Three-dimensional learning"
Xiang, Mingjun, Lingxiao Wang, Yu Sha, Hui Yuan, Kai Zhou, and Hartmut G. Roskos. "Phase Retrieval for Terahertz Holography with Physics-Informed Deep Learning." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/dh.2022.tu4a.4.
Повний текст джерелаXiong, Chencen, Zhenbo Ren, Jianglei Di, and Jianlin Zhao. "Phase imaging for digital holography with deep learning." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/dh.2022.w5a.52.
Повний текст джерелаMiao, Xin, Xin Yuan, and Paul Wilford. "Deep Learning for Compressive Spectral Imaging." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/dh.2019.m3b.3.
Повний текст джерелаRahmani, Babak, Damien Loterie, Eirini Kakkava, Navid Borhani, Ugur Tegin, Demetri Psaltis, and Christophe Moser. "Multimode fiber projection with machine learning." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/dh.2020.htu5b.3.
Повний текст джерелаShui, Xinghua, and Huadong Zheng. "Multi-depth Hologram Generation with Unsupervised-learning Based Computer-generated Holography." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/dh.2022.w5a.12.
Повний текст джерелаChia, Yu-Hsin, Sunil Vyas, Yi-You Huang, and Yuan Luo. "Deep learning based HiLo optical sectioning imaging." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/dh.2022.w5a.19.
Повний текст джерелаWang, Hao, Meng Lyu, Ni Chen, and Guohai Situ. "In-line hologram reconstruction with deep learning." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/dh.2018.dw2f.2.
Повний текст джерелаNguyen, Thanh, Vy Bui, and George Nehmetallah. "3D Optical Diffraction Tomography Using Deep Learning." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/dh.2018.dw2f.4.
Повний текст джерелаChoo, Hyon-Gon, Yeon-Gyeong Ju, Kwan-Jung Oh, Yongjun Lim, and Jae-Hyeung Park. "Hologram Reconstruction using cascaded deep learning networks." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2021. http://dx.doi.org/10.1364/dh.2021.df4c.3.
Повний текст джерелаMoon, Junbeom, Kihong Choi, Keehoon Hong, Joongki Park, and Soon Ki Jung. "Learning-based noise reduction method for incoherent digital holography." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/dh.2022.th4a.1.
Повний текст джерелаЗвіти організацій з теми "Three-dimensional learning"
Osadchyi, Viacheslav V., Hanna Y. Chemerys, Kateryna P. Osadcha, Vladyslav S. Kruhlyk, Serhii L. Koniukhov, and Arnold E. Kiv. Conceptual model of learning based on the combined capabilities of augmented and virtual reality technologies with adaptive learning systems. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4417.
Повний текст джерелаKompaniets, Alla, Hanna Chemerys, and Iryna Krasheninnik. Using 3D modelling in design training simulator with augmented reality. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3740.
Повний текст джерела