Academic literature on the topic 'Visual optimization'
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Journal articles on the topic "Visual optimization"
McLin, L. N., B. P. Goettl, L. E. Barnes, F. H. Previc, and G. T. Hengst. "Visual warning signal optimization." Journal of Vision 5, no. 12 (December 1, 2005): 75. http://dx.doi.org/10.1167/5.12.75.
Full textGuo, Fei, Yuan Yang, and Yong Gao. "Optimization of Visual Information Presentation for Visual Prosthesis." International Journal of Biomedical Imaging 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/3198342.
Full textGepshtein, Sergei, and Thomas D. Albright. "Adaptive Optimization of Visual Sensitivity." Journal of the Indian Institute of Science 97, no. 4 (November 25, 2017): 423–34. http://dx.doi.org/10.1007/s41745-017-0056-y.
Full textSivertsen, Edvard, Frøydis Bjerke, Trygve Almøy, Vegard Segtnan, and Tormod Næs. "Multivariate optimization by visual inspection." Chemometrics and Intelligent Laboratory Systems 85, no. 1 (January 2007): 110–18. http://dx.doi.org/10.1016/j.chemolab.2006.05.005.
Full textMukeshbhaiKamalakannan J, Kansagra Deep. "Optimization Through Visual Enhancement of Compression Algorithm for Image in JPEG2000 Standard." Indian Journal of Applied Research 4, no. 8 (October 1, 2011): 191–94. http://dx.doi.org/10.15373/2249555x/august2014/53.
Full textTweed, Douglas. "Visual-motor optimization in binocular control." Vision Research 37, no. 14 (July 1997): 1939–51. http://dx.doi.org/10.1016/s0042-6989(97)00002-3.
Full textZhou Wei, W., M. Moore, and F. Kussener. "Visual tolerance analysis for engineering optimization." International Journal of Metrology and Quality Engineering 4, no. 3 (2013): 153–62. http://dx.doi.org/10.1051/ijmqe/2013056.
Full textVirtanen, K., H. Ehtamo, T. Raivio, and R. P. Hamalainen. "VIATO-visual interactive aircraft trajectory optimization." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 29, no. 3 (1999): 409–21. http://dx.doi.org/10.1109/5326.777076.
Full textTorreão, JoséR A., and Edward Roe. "Microcanonical optimization applied to visual processing." Physics Letters A 205, no. 5-6 (September 1995): 377–82. http://dx.doi.org/10.1016/0375-9601(95)00585-q.
Full textJun Ding, Jun Ding, Mali Liu Mali Liu, Qing Zhong Qing Zhong, Haifeng Li Haifeng Li, and and Xu Liu and Xu Liu. "Optimization algorithm of near-eye light field displays based on human visual characteristics." Chinese Optics Letters 14, no. 4 (2016): 041101–41105. http://dx.doi.org/10.3788/col201614.041101.
Full textDissertations / Theses on the topic "Visual optimization"
Timm, Richard W. (Richard William). "Visual-based methods in compliant mechanism optimization." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35649.
Full textIncludes bibliographical references (p. 103-105).
The purpose of this research is to generate visual-based methods for optimizing compliant mechanisms (CMs). Visual-based optimization methods use graphical representations (3-D plots) of CM performance to convey design information. They have many advantages over traditional optimization methods, such as enabling judgment-based design tradeoffs and ensuring robustness of optimized solutions. This research fulfilled the primary aims of determining (1) how to best convey decision-driving design information, and (2) how to interpret and analyze the results of a visual-based optimization method. Other useful tools resulting from this work are (3) a nondimensional model of a CM (a compliant four-bar mechanism) that may be used to maximize the information density of optimization plots, and (4) a new model of a compliant beam that establishes a link between beam stiffness and instant center location. This work presents designers with an optimization tool that may either be used to augment or replace current optimization methods.
by Richard W. Timm.
S.M.
Ahmad, Naeem. "Modelling and optimization of sky surveillance visual sensor network." Licentiate thesis, Mittuniversitetet, Institutionen för informationsteknologi och medier, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-17123.
Full textChung, Ka Kei. "Interactive visual optimization and analysis for RFID system performance /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20CHUNG.
Full textGisginis, Alexandros. "Production line optimization featuring cobots and visual inspection system." Thesis, Blekinge Tekniska Högskola, Institutionen för maskinteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21752.
Full textTreptow, André. "Optimization techniques for real time visual object detection and tracking." Berlin Logos-Verl, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=2938420&prov=M&dok_var=1&dok_ext=htm.
Full textChen, Zhaozhong. "Visual-Inertial SLAM Extrinsic Parameter Calibration Based on Bayesian Optimization." Thesis, University of Colorado at Boulder, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10789260.
Full textVI-SLAM (Visual-Inertial Simultaneous Localization and Mapping) is a popular way for robotics navigation and tracking. With the help of sensor fusion from IMU and camera, VI-SLAM can give a more accurate solution for navigation. One important problem needs to be solved in VI-SLAM is that we need to know accurate relative position between camera and IMU, we call it the extrinsic parameter. However, our measurement of the rotation and translation between IMU and camera is noisy. If the measurement is slightly o?, the result of SLAM system will be much more away from the ground truth after a long run. Optimization is necessary. This paper uses a global optimization method called Bayesian Optimization to optimize the relative pose between IMU and camera based on the sliding window residual output from VISLAM. The advantage of using Bayesian Optimization is that we can get an accurate pose estimation between IMU and camera from a large searching range. Whats more, thanks to the Gaussian Process or T process of Bayesian Optimization, we can get a result with a known uncertainty, which cannot be done by many optimization solutions.
Verpers, Felix. "Improving a stereo-based visual odometry prototype with global optimization." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-383268.
Full textAwang, Salleh Dayang Nur Salmi Dharmiza. "Study of vehicle localization optimization with visual odometry trajectory tracking." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS601.
Full textWith the growing research on Advanced Driver Assistance Systems (ADAS) for Intelligent Transport Systems (ITS), accurate vehicle localization plays an important role in intelligent vehicles. The Global Positioning System (GPS) has been widely used but its accuracy deteriorates and susceptible to positioning error due to factors such as the restricting environments that results in signal weakening. This problem can be addressed by integrating the GPS data with additional information from other sensors. Meanwhile, nowadays, we can find vehicles equipped with sensors for ADAS applications. In this research, fusion of GPS with visual odometry (VO) and digital map is proposed as a solution to localization improvement with low-cost data fusion. From the published works on VO, it is interesting to know how the generated trajectory can further improve vehicle localization. By integrating the VO output with GPS and OpenStreetMap (OSM) data, estimates of vehicle position on the map can be obtained. The lateral positioning error is reduced by utilizing lane distribution information provided by OSM while the longitudinal positioning is optimized with curve matching between VO trajectory trail and segmented roads. To observe the system robustness, the method was validated with KITTI datasets tested with different common GPS noise. Several published VO methods were also used to compare improvement level after data fusion. Validation results show that the positioning accuracy achieved significant improvement especially for the longitudinal error with curve matching technique. The localization performance is on par with Simultaneous Localization and Mapping (SLAM) SLAM techniques despite the drift in VO trajectory input. The research on employability of VO trajectory is extended for a deterministic task in lane-change detection. This is to assist the routing service for lane-level direction in navigation. The lane-change detection was conducted by CUSUM and curve fitting technique that resulted in 100% successful detection for stereo VO. Further study for the detection strategy is however required to obtain the current true lane of the vehicle for lane-level accurate localization. With the results obtained from the proposed low-cost data fusion for localization, we see a bright prospect of utilizing VO trajectory with information from OSM to improve the performance. In addition to obtain VO trajectory, the camera mounted on the vehicle can also be used for other image processing applications to complement the system. This research will continue to develop with future works concluded in the last chapter of this thesis
Elsidani, Elariss Haifa. "A new visual query language and query optimization for mobile GPS." Thesis, Kingston University, 2008. http://eprints.kingston.ac.uk/20306/.
Full textHernandez, Herrero Sandra. "Cross-layer optimization for visual-inertial localization on resource-constrained devices." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296834.
Full textMobila enheter med begränsade resurser, som drönare och rovers, förväntas stödja mer och mer krävande cyberfysiska applikationer. Glappet mellan enheternas begränsningar i hårdvara och applikationskrav är dock fortfarande stort - motstridiga mål som robust, noggrann och effektiv körning måste uppfyllas för att uppnå acceptabel drift. Detta examensarbete undersöker avvägningen mellan prestanda och effektivitet i cyberfysiska system, särskilt med avseende på lokalisering som är en av de viktigaste uppgifterna för alla mobila autonoma enheter. Vi gör en design space exploration (DSE) genom att variera ett antal parametrar för både lokaliseringsalgoritm och plattformslager. Baserat på konfigurationsrummet formulerar vi ett tvärlageroptimeringsproblem med flera mål för att utforska avvägningen mellan noggrannhet i lokaliseringen och energiåtgång. I våra experiment kör vi maplab – ett visuellt tröghetsramverk för lokalisering och kartläggning – på Nvidia Jetson AGXoch NX-plattformarna. Vi presenterar sedan en robust prediktiv modell som kan användas för att välja konfigurationer vid körning i en föränderlig miljö.
Books on the topic "Visual optimization"
Timothy, Koets, ed. Visual Basic 4 performance tuning and optimization. Indianapolis, Ind: Sams Pub., 1995.
Find full textNemzow, Martin A. W. Visual Basic developer's toolkit: Performance optimization, rapid application development, debugging, and distribution. New York: McGraw-Hill, 1996.
Find full textSearch engine optimization: Your visual blueprint for effective internet marketing. 2nd ed. Indianapolis, IN: Wiley, 2010.
Find full textSearch engine optimization: Your visual blueprint for effective Internet marketing. 3rd ed. Hoboken, N.J: Visual/John Wiley & Sons, 2013.
Find full textauthor, Velinov Ivo, ed. Online Visual Merchandising: Structural Elements And Optimization For Apparel Web Stores. Saarbrücken: LAP LAMBERT Academic Publishing, 2014.
Find full textAlexander, R. C++ footprint and performance optimization. Indianapolis, Ind: Sams, 2000.
Find full textZnO bao mo zhi bei ji qi guang, dian xing neng yan jiu. Shanghai Shi: Shanghai da xue chu ban she, 2010.
Find full textMagda, Yury. Visual C++ Optimization with Assembly Code. A-List Publishing, 2004.
Find full textSearch Engine Optimization: Your visual blueprint to effective Internet marketing (Visual Blueprint). Visual, 2008.
Find full textBosch, Robert. Opt Art: From Mathematical Optimization to Visual Design. Princeton University Press, 2019.
Find full textBook chapters on the topic "Visual optimization"
Kurniawan, Budi. "Database Access Optimization." In Internet Programming with Visual Basic, 141–80. Berkeley, CA: Apress, 2000. http://dx.doi.org/10.1007/978-1-4302-1144-0_5.
Full textZemmari, Akka, and Jenny Benois-Pineau. "Optimization Methods." In Deep Learning in Mining of Visual Content, 21–33. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34376-7_4.
Full textWan, Shenghua, Tengfei Ye, Maoqing Li, Hongchao Zhang, and Xin Li. "Efficient Spherical Parametrization Using Progressive Optimization." In Computational Visual Media, 170–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34263-9_22.
Full textXia, Tian, and Eric Shaffer. "Streaming Mesh Optimization for CAD." In Advances in Visual Computing, 1022–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89646-3_102.
Full textHe, Jiaxi, Cishen Zhang, and Ifat-Al Baqee. "Image Dehazing Using Regularized Optimization." In Advances in Visual Computing, 87–96. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14249-4_9.
Full textSotskova, Nadezhda, Jörg Haustein, Winfried Jäicke, and Wolfgang Thämelt. "Visual Representation of Material Flows in Chemical Plant Systems." In Applied Optimization, 247–61. Boston, MA: Springer US, 2005. http://dx.doi.org/10.1007/0-387-23581-7_18.
Full textRuseckaite, Rasa. "Computational Methods for Epilepsy Diagnosis. Visual Perception and EEG." In Applied Optimization, 259–80. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4613-0233-9_11.
Full textHynes, Andrew, and Stephen Czarnuch. "Combinatorial Optimization for Human Body Tracking." In Advances in Visual Computing, 524–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50832-0_51.
Full textJoshua Thomas, J., Bahari Belaton, Ahamad Tajudin Khader, and Justtina. "Visual Analytics Solution for Scheduling Processing Phases." In Intelligent Computing & Optimization, 395–408. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00979-3_42.
Full textPapazov, Chavdar, and Darius Burschka. "Stochastic Optimization for Rigid Point Set Registration." In Advances in Visual Computing, 1043–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10331-5_97.
Full textConference papers on the topic "Visual optimization"
Sun, M., and K. Webb. "Visual Optimization with Domain Reduction." In 2007 Thirty-Ninth Southeastern Symposium on System Theory. IEEE, 2007. http://dx.doi.org/10.1109/ssst.2007.352371.
Full textTazebay, Mehmet V., and Ali N. Akansu. "Progressive optimization in subband trees." In Visual Communications and Image Processing '94, edited by Aggelos K. Katsaggelos. SPIE, 1994. http://dx.doi.org/10.1117/12.185869.
Full textDougherty, Edward R., Robert M. Haralick, Yidong Chen, Bo Li, Carsten Agerskov, Ulrik Jacobi, and Poul H. Sloth. "Morphological pattern-spectra-based Tau-opening optimization." In Visual Communications, '91, Boston, MA, edited by Kou-Hu Tzou and Toshio Koga. SPIE, 1991. http://dx.doi.org/10.1117/12.50386.
Full textWiner, E., and C. Bloebaum. "Visual design steering for optimization solution improvement." In 8th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-4815.
Full textMilana Huang, R. P. Grzeszczuk, and L. H. Kauffman. "Untangling knots by stochastic energy optimization." In Proceedings of Seventh Annual IEEE Visualization '96. IEEE, 1996. http://dx.doi.org/10.1109/visual.1996.568120.
Full textHemalatha, G., and B. Anuradha. "Enhancement of visual evoked potentials." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016. http://dx.doi.org/10.1109/iceeot.2016.7754835.
Full textWang, Shiqi, Siwei Ma, and Wen Gao. "SSIM based perceptual distortion rate optimization coding." In Visual Communications and Image Processing 2010, edited by Pascal Frossard, Houqiang Li, Feng Wu, Bernd Girod, Shipeng Li, and Guo Wei. SPIE, 2010. http://dx.doi.org/10.1117/12.863467.
Full textSriranganathan, S., David R. Bull, and David W. Redmill. "Optimization of multiplierless two-dimensional digital filters." In Visual Communications and Image Processing '96, edited by Rashid Ansari and Mark J. T. Smith. SPIE, 1996. http://dx.doi.org/10.1117/12.233201.
Full textJ.R, Siddiqui, and Khatibi S. "Visual Tracking Using Particle Swarm Optimization." In International Conference on Foundations of Computer Science & Technology. Academy & Industry Research Collaboration Center (AIRCC), 2014. http://dx.doi.org/10.5121/csit.2014.4126.
Full textHu, Zhengyi, and Qingchang Tan. "Optimization Based Mono-Visual Inertial Odometry." In 2018 5th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2018. http://dx.doi.org/10.1109/icisce.2018.00082.
Full textReports on the topic "Visual optimization"
Tannenbaum, Allen R. Geometric PDE's and Invariants for Problems in Visual Control Tracking and Optimization. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada428955.
Full textMARTINEZ, RUBEL F. A Pattern Recognition Feature Optimization Tool Using the Visual Empirical Region of Influence Algorithm. Office of Scientific and Technical Information (OSTI), June 2002. http://dx.doi.org/10.2172/800997.
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