Bibliografie tematiche / Visual optimization

Letteratura scientifica selezionata sul tema "Visual optimization"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 10 febbraio 2022

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Articoli di riviste sul tema "Visual optimization"

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McLin, L. N., B. P. Goettl, L. E. Barnes, F. H. Previc e G. T. Hengst. "Visual warning signal optimization". Journal of Vision 5, n. 12 (1 dicembre 2005): 75. http://dx.doi.org/10.1167/5.12.75.

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Guo, Fei, Yuan Yang e 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.

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Visual prosthesis applying electrical stimulation to restore visual function for the blind has promising prospects. However, due to the low resolution, limited visual field, and the low dynamic range of the visual perception, huge loss of information occurred when presenting daily scenes. The ability of object recognition in real-life scenarios is severely restricted for prosthetic users. To overcome the limitations, optimizing the visual information in the simulated prosthetic vision has been the focus of research. This paper proposes two image processing strategies based on a salient object detection technique. The two processing strategies enable the prosthetic implants to focus on the object of interest and suppress the background clutter. Psychophysical experiments show that techniques such as foreground zooming with background clutter removal and foreground edge detection with background reduction have positive impacts on the task of object recognition in simulated prosthetic vision. By using edge detection and zooming technique, the two processing strategies significantly improve the recognition accuracy of objects. We can conclude that the visual prosthesis using our proposed strategy can assist the blind to improve their ability to recognize objects. The results will provide effective solutions for the further development of visual prosthesis.
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Gepshtein, Sergei, e Thomas D. Albright. "Adaptive Optimization of Visual Sensitivity". Journal of the Indian Institute of Science 97, n. 4 (25 novembre 2017): 423–34. http://dx.doi.org/10.1007/s41745-017-0056-y.

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Sivertsen, Edvard, Frøydis Bjerke, Trygve Almøy, Vegard Segtnan e Tormod Næs. "Multivariate optimization by visual inspection". Chemometrics and Intelligent Laboratory Systems 85, n. 1 (gennaio 2007): 110–18. http://dx.doi.org/10.1016/j.chemolab.2006.05.005.

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MukeshbhaiKamalakannan J, Kansagra Deep. "Optimization Through Visual Enhancement of Compression Algorithm for Image in JPEG2000 Standard". Indian Journal of Applied Research 4, n. 8 (1 ottobre 2011): 191–94. http://dx.doi.org/10.15373/2249555x/august2014/53.

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Tweed, Douglas. "Visual-motor optimization in binocular control". Vision Research 37, n. 14 (luglio 1997): 1939–51. http://dx.doi.org/10.1016/s0042-6989(97)00002-3.

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Zhou Wei, W., M. Moore e F. Kussener. "Visual tolerance analysis for engineering optimization". International Journal of Metrology and Quality Engineering 4, n. 3 (2013): 153–62. http://dx.doi.org/10.1051/ijmqe/2013056.

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Virtanen, K., H. Ehtamo, T. Raivio e R. P. Hamalainen. "VIATO-visual interactive aircraft trajectory optimization". IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 29, n. 3 (1999): 409–21. http://dx.doi.org/10.1109/5326.777076.

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Torreão, JoséR A., e Edward Roe. "Microcanonical optimization applied to visual processing". Physics Letters A 205, n. 5-6 (settembre 1995): 377–82. http://dx.doi.org/10.1016/0375-9601(95)00585-q.

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Jun Ding, Jun Ding, Mali Liu Mali Liu, Qing Zhong Qing Zhong, Haifeng Li Haifeng Li e and Xu Liu and Xu Liu. "Optimization algorithm of near-eye light field displays based on human visual characteristics". Chinese Optics Letters 14, n. 4 (2016): 041101–41105. http://dx.doi.org/10.3788/col201614.041101.

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Tesi sul tema "Visual optimization"

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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.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.
Includes 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.
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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.

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A Visual Sensor Network (VSN) is a distributed system of a largenumber of camera sensor nodes. The main components of a camera sensornode are image sensor, embedded processor, wireless transceiver and energysupply. The major difference between a VSN and an ordinary sensor networkis that a VSN generates two dimensional data in the form of an image, whichcan be exploited in many useful applications. Some of the potentialapplication examples of VSNs include environment monitoring, surveillance,structural monitoring, traffic monitoring, and industrial automation.However, the VSNs also raise new challenges. They generate large amount ofdata which require higher processing powers, large bandwidth requirementsand more energy resources but the main constraint is that the VSN nodes arelimited in these resources.This research focuses on the development of a VSN model to track thelarge birds such as Golden Eagle in the sky. The model explores a number ofcamera sensors along with optics such as lens of suitable focal length whichensures a minimum required resolution of a bird, flying at the highestaltitude. The combination of a camera sensor and a lens formulate amonitoring node. The camera node model is used to optimize the placementof the nodes for full coverage of a given area above a required lower altitude.The model also presents the solution to minimize the cost (number of sensornodes) to fully cover a given area between the two required extremes, higherand lower altitudes, in terms of camera sensor, lens focal length, camera nodeplacement and actual number of nodes for sky surveillance.The area covered by a VSN can be increased by increasing the highermonitoring altitude and/or decreasing the lower monitoring altitude.However, it also increases the cost of the VSN. The desirable objective is toincrease the covered area but decrease the cost. This objective is achieved byusing optimization techniques to design a heterogeneous VSN. The core ideais to divide a given monitoring range of altitudes into a number of sub-rangesof altitudes. The sub-ranges of monitoring altitudes are covered by individualsub VSNs, the VSN1 covers the lower sub-range of altitudes, the VSN2 coversthe next higher sub-range of altitudes and so on, such that a minimum cost isused to monitor a given area.To verify the concepts, developed to design the VSN model, and theoptimization techniques to decrease the VSN cost, the measurements areperformed with actual cameras and optics. The laptop machines are used withthe camera nodes as data storage and analysis platforms. The area coverage ismeasured at the desired lower altitude limits of homogeneous as well asheterogeneous VSNs and verified for 100% coverage. Similarly, the minimumresolution is measured at the desired higher altitude limits of homogeneous aswell as heterogeneous VSNs to ensure that the models are able to track thebird at these highest altitudes.
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Chung, 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.

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Gisginis, 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.

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This study examines the automatization potential for two production lines at Scania Transmission workshop in Södertälje using Industry 4.0 technologies. In order to do so, the capabilities of the operational performance and safety features of cobots and an Automated Visual Inspection System is theoretically investigated and intended to substitute CNC operators on certain tasks such as loading of conveyors and quality controls. The purpose of the study is to generate a realistic approach and give insight to the benefits of a future practical implementation.Previous research around these technologies as well as the actual data recordings and several interviews that took place during on-site visits is presented. The results show that a significant amount of time can be saved and allocated differently. Based on the findings of the study, a layout for the cobots and AVIS placement is proposed, aiming for CNC operator’s better control over the critical parts of the production lines, thus contributing to a much more manageable workflow.
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Treptow, 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.

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Chen, 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.

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VI-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.

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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.

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In this degree project global optimization methods, for a previously developedsoftwareprototype of a stereo odometry system, were studied. The existing softwareestimatesthe motion between stereo frames and builds up a map of selected stereo frameswhich accumulates increasing error over time. The aim of the project was to studymethods to mitigate the error accumulated over time in the step-wise motionestimation.One approach based on relative pose estimates and another approach based onreprojection optimization were implemented and evaluated for the existing platform.The results indicate that optimization based on relative keyframe estimates ispromising for real-time usage. The second strategy based on reprojection of stereotriangulatedpoints proved useful as a refinement step but the relatively small errorreduction comes at an increased computational cost. Therefore, this approachrequiresfurther improvements to become applicable in situations where corrections areneededin real-time, and it is hard to justify the increased computations for the relatively smallerror reduction.The results also show that the global optimization primarily improves the absolutetrajectory error.
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Awang, 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.

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Au sein des systèmes avancés d’aide à la conduite (Advanced Driver Assistance Systems - ADAS) pour les systèmes de transport intelligents (Intelligent Transport Systems - ITS), les systèmes de positionnement, ou de localisation, du véhicule jouent un rôle primordial. Le système GPS (Global Positioning System) largement employé ne peut donner seul un résultat précis à cause de facteurs extérieurs comme un environnement contraint ou l’affaiblissement des signaux. Ces erreurs peuvent être en partie corrigées en fusionnant les données GPS avec des informations supplémentaires provenant d'autres capteurs. La multiplication des systèmes d’aide à la conduite disponibles dans les véhicules nécessite de plus en plus de capteurs installés et augmente le volume de données utilisables. Dans ce cadre, nous nous sommes intéressés à la fusion des données provenant de capteurs bas cout pour améliorer le positionnement du véhicule. Parmi ces sources d’information, en parallèle au GPS, nous avons considérés les caméras disponibles sur les véhicules dans le but de faire de l’odométrie visuelle (Visual Odometry - VO), couplée à une carte de l’environnement. Nous avons étudié les caractéristiques de cette trajectoire reconstituée dans le but d’améliorer la qualité du positionnement latéral et longitudinal du véhicule sur la route, et de détecter les changements de voies possibles. Après avoir été fusionnée avec les données GPS, cette trajectoire générée est couplée avec la carte de l’environnement provenant d’Open-StreetMap (OSM). L'erreur de positionnement latérale est réduite en utilisant les informations de distribution de voie fournies par OSM, tandis que le positionnement longitudinal est optimisé avec une correspondance de courbes entre la trajectoire provenant de l’odométrie visuelle et les routes segmentées décrites dans OSM. Pour vérifier la robustesse du système, la méthode a été validée avec des jeux de données KITTI en considérant des données GPS bruitées par des modèles de bruits usuels. Plusieurs méthodes d’odométrie visuelle ont été utilisées pour comparer l’influence de la méthode sur le niveau d'amélioration du résultat après fusion des données. En utilisant la technique d’appariement des courbes que nous proposons, la précision du positionnement connait une amélioration significative, en particulier pour l’erreur longitudinale. Les performances de localisation sont comparables à celles des techniques SLAM (Simultaneous Localization And Mapping), corrigeant l’erreur d’orientation initiale provenant de l’odométrie visuelle. Nous avons ensuite employé la trajectoire provenant de l’odométrie visuelle dans le cadre de la détection de changement de voie. Cette indication est utile dans pour les systèmes de navigation des véhicules. La détection de changement de voie a été réalisée par une somme cumulative et une technique d’ajustement de courbe et obtient de très bon taux de réussite. Des perspectives de recherche sur la stratégie de détection sont proposées pour déterminer la voie initiale du véhicule. En conclusion, les résultats obtenus lors de ces travaux montrent l’intérêt de l’utilisation de la trajectoire provenant de l’odométrie visuelle comme source d’information pour la fusion de données à faible coût pour la localisation des véhicules. Cette source d’information provenant de la caméra est complémentaire aux données d’images traitées qui pourront par ailleurs être utilisées pour les différentes taches visée par les systèmes d’aides à la conduite
With 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
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Elsidani, Elariss Haifa. "A new visual query language and query optimization for mobile GPS". Thesis, Kingston University, 2008. http://eprints.kingston.ac.uk/20306/.

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In recent years computer applications have been deployed to manage spatial data with Geographic Information Systems (GIS) to store and analyze data related to domains such as transportation and tourism. Recent developments have shown that there is an urgent need to develop systmes for mobile devices and particularly for Location Based Services (LBS) such as proximity analysis that helps in finding the nearest neighbors, for example. restaurant, and the facilities that are located within a circle area around the user's location, known as a buffer area, for example, all restaurants within 100 meters. The mobile market potential is across geographical and cultural boundaries. Hence the visualization of queries becomes important especially that the existing visual query languages have a number of limitations. They are not tailored for mobile GIS and they do not support dynamic complex queries (DCQ) and visual query formation. Thus, the first aim of this research is to develop a new visual query language (IVQL) for mobile GIS that handles static and DCQ for proximity analysis. IVQL is designed and implemented using smiley icons that visualize operators, values, and objects. The evaluation results reveal that it has an expressive power, easy-to-use user interface, easy query building, and a high user satisfaction. There is also a need that new optimization strategies consider the scale of mobile user queries. Existing query optimization strategies are based on the sharing and push-down paradigms and they do not cover multiple-DCQ (MDCQ) for proximity analysis. This leads to the second aim of this thesis which is to develop the query melting processor (QMP) that is responsible for processing MDCQs. QMP is based on the new Query Melting paradigm which consists of the sharing paradigm, query optimization, and is implemented by a new strategy "Melting Ruler". Moreover, with the increase in volume of cost sensitive mobile users, the need emerges to develop a time cost optimizer for processing MDCQs. Thus, the thirs aim of the thesis is to develop a new Decision Making Mechanism for time cost optimization (TCOP) and prove its cost effectiveness. TCOP is based on the new paradigm "Sharing global execution plans by MDCQs with similar scenarios". The experimental evaluation results, using a case study based on the map of Paris, proved that significant saving in time can be achieved by employing the newly developed strategies.
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Hernandez, 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.

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Mobile devices are increasingly expected to support high-performance cyber- physical applications in resource-constrained devices, such as drones and rovers. However, the gap between hardware limitations of these devices and application requirements is still prohibitive – conflicting goals such as robust, accurate, and efficient execution must be managed carefully to achieve acceptable operation. This thesis is focus on the exploration of the tradeoff between performance and efficiency in such cyber-physical systems, specifically with respect to localization, a core task for any mobile autonomous device. We perform a design space exploration (DSE) given a number of configurable parameters for both localization algorithm and platform layers. Given the configuration space, we formulate a cross-layer multi-objective optimization problem to explore the tradeoff between localization accuracy and power consumption. For our experiments we execute maplab – a visual-inertial localization and mapping framework – monolithically on the Nvidia Jetson AGX and NX platforms. We then propose a predictive model for robust execution that can be used to determine desirable configurations at runtime in the face of environmental changes.
Mobila 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.
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Libri sul tema "Visual optimization"

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Timothy, Koets, a cura di. Visual Basic 4 performance tuning and optimization. Indianapolis, Ind: Sams Pub., 1995.

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Nemzow, Martin A. W. Visual Basic developer's toolkit: Performance optimization, rapid application development, debugging, and distribution. New York: McGraw-Hill, 1996.

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Search engine optimization: Your visual blueprint for effective internet marketing. 2a ed. Indianapolis, IN: Wiley, 2010.

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Search engine optimization: Your visual blueprint for effective Internet marketing. 3a ed. Hoboken, N.J: Visual/John Wiley & Sons, 2013.

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author, Velinov Ivo, a cura di. Online Visual Merchandising: Structural Elements And Optimization For Apparel Web Stores. Saarbrücken: LAP LAMBERT Academic Publishing, 2014.

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Alexander, R. C++ footprint and performance optimization. Indianapolis, Ind: Sams, 2000.

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ZnO bao mo zhi bei ji qi guang, dian xing neng yan jiu. Shanghai Shi: Shanghai da xue chu ban she, 2010.

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Magda, Yury. Visual C++ Optimization with Assembly Code. A-List Publishing, 2004.

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Search Engine Optimization: Your visual blueprint to effective Internet marketing (Visual Blueprint). Visual, 2008.

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Bosch, Robert. Opt Art: From Mathematical Optimization to Visual Design. Princeton University Press, 2019.

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Capitoli di libri sul tema "Visual optimization"

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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.

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Zemmari, Akka, e 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.

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Wan, Shenghua, Tengfei Ye, Maoqing Li, Hongchao Zhang e 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.

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Xia, Tian, e 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.

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He, Jiaxi, Cishen Zhang e 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.

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Sotskova, Nadezhda, Jörg Haustein, Winfried Jäicke e 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.

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Ruseckaite, 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.

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Hynes, Andrew, e 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.

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Joshua Thomas, J., Bahari Belaton, Ahamad Tajudin Khader e 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.

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Papazov, Chavdar, e 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.

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Atti di convegni sul tema "Visual optimization"

1

Sun, M., e 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.

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Tazebay, Mehmet V., e Ali N. Akansu. "Progressive optimization in subband trees". In Visual Communications and Image Processing '94, a cura di Aggelos K. Katsaggelos. SPIE, 1994. http://dx.doi.org/10.1117/12.185869.

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Dougherty, Edward R., Robert M. Haralick, Yidong Chen, Bo Li, Carsten Agerskov, Ulrik Jacobi e Poul H. Sloth. "Morphological pattern-spectra-based Tau-opening optimization". In Visual Communications, '91, Boston, MA, a cura di Kou-Hu Tzou e Toshio Koga. SPIE, 1991. http://dx.doi.org/10.1117/12.50386.

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Winer, E., e 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.

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5

Milana Huang, R. P. Grzeszczuk e 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.

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Hemalatha, G., e 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.

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Wang, Shiqi, Siwei Ma e Wen Gao. "SSIM based perceptual distortion rate optimization coding". In Visual Communications and Image Processing 2010, a cura di Pascal Frossard, Houqiang Li, Feng Wu, Bernd Girod, Shipeng Li e Guo Wei. SPIE, 2010. http://dx.doi.org/10.1117/12.863467.

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Sriranganathan, S., David R. Bull e David W. Redmill. "Optimization of multiplierless two-dimensional digital filters". In Visual Communications and Image Processing '96, a cura di Rashid Ansari e Mark J. T. Smith. SPIE, 1996. http://dx.doi.org/10.1117/12.233201.

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J.R, Siddiqui, e 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.

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Hu, Zhengyi, e 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.

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Rapporti di organizzazioni sul tema "Visual optimization"

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Tannenbaum, Allen R. Geometric PDE's and Invariants for Problems in Visual Control Tracking and Optimization. Fort Belvoir, VA: Defense Technical Information Center, gennaio 2005. http://dx.doi.org/10.21236/ada428955.

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MARTINEZ, RUBEL F. A Pattern Recognition Feature Optimization Tool Using the Visual Empirical Region of Influence Algorithm. Office of Scientific and Technical Information (OSTI), giugno 2002. http://dx.doi.org/10.2172/800997.

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