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Articoli di riviste sul tema "Visual optimization"

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

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1

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.

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Guo, 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.

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Abstract (sommario):
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
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Gepshtein, 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.

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Sivertsen, 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.

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5

MukeshbhaiKamalakannan 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.

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6

Tweed, 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.

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7

Zhou 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.

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8

Virtanen, 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.

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Torreão, 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.

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10

Jun 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.

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Osipov, Vasily Yurevich, and Victor Victorovich Kuzenny. "Optimization of visual environment for spatial modeling." SPIIRAS Proceedings 1, no. 12 (March 17, 2014): 235. http://dx.doi.org/10.15622/sp.12.16.

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Vasiliev, Petr Victorovich, and Victor Victorovich Kuzenny. "Optimization of visual environment for spatial modeling." SPIIRAS Proceedings 4, no. 19 (March 17, 2014): 288. http://dx.doi.org/10.15622/sp.19.16.

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13

Zeng, Qiong, Wenzheng Chen, Zhuo Han, Mingyi Shi, Yanir Kleiman, Daniel Cohen-Or, Baoquan Chen, and Yangyan Li. "Group optimization for multi-attribute visual embedding." Visual Informatics 2, no. 3 (September 2018): 181–89. http://dx.doi.org/10.1016/j.visinf.2018.09.004.

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14

Okunev, O. V., S. A. Fedorov, and D. S. Fukalov. "Visual quality optimization for real-time encoders." Pattern Recognition and Image Analysis 20, no. 2 (June 2010): 185–91. http://dx.doi.org/10.1134/s1054661810020100.

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15

Choi, K. H., and J. N. Hwang. "Constrained Optimization for Audio-to-Visual Conversion." IEEE Transactions on Signal Processing 52, no. 6 (June 2004): 1783–90. http://dx.doi.org/10.1109/tsp.2004.827153.

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16

Koushik, Jayanth, Austin Marcus, Aarti Singh, and Michael Tarr. "Real-time Optimization for Visual Feature Identification." Journal of Vision 18, no. 10 (September 1, 2018): 413. http://dx.doi.org/10.1167/18.10.413.

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17

Wang, Wei, Zhaoming Chen, and Mingquan Shi. "Visual tracking with complementary deep feature optimization." Journal of Electronic Imaging 27, no. 04 (August 27, 2018): 1. http://dx.doi.org/10.1117/1.jei.27.4.043052.

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18

Jia Li, Yonghong Tian, Lingyu Duan, and Tiejun Huang. "Estimating Visual Saliency Through Single Image Optimization." IEEE Signal Processing Letters 20, no. 9 (September 2013): 845–48. http://dx.doi.org/10.1109/lsp.2013.2268868.

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19

Lissandrini, Nicola, Giulia Michieletto, Riccardo Antonello, Marta Galvan, Alberto Franco, and Angelo Cenedese. "Cooperative Optimization of UAVs Formation Visual Tracking." Robotics 8, no. 3 (July 7, 2019): 52. http://dx.doi.org/10.3390/robotics8030052.

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Abstract (sommario):
The use of unmanned vehicles to perform tiring, hazardous, repetitive tasks, is becoming a reality out of the academy laboratories, getting more and more interest for several application fields from the industrial, to the civil, to the military contexts. In particular, these technologies appear quite promising when they employ several low-cost resource-constrained vehicles leveraging their coordination to perform complex tasks with efficiency, flexibility, and adaptation that are superior to those of a single agent (even if more instrumented). In this work, we study one of said applications, n
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20

Nguyen, Giang Phuong, and Marcel Worring. "Optimization of interactive visual-similarity-based search." ACM Transactions on Multimedia Computing, Communications, and Applications 4, no. 1 (January 2008): 1–23. http://dx.doi.org/10.1145/1324287.1324294.

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21

Tao, Yizhang, Yiyi Shen, Bin Sheng, Ping Li, and Rynson W. H. Lau. "Video Decolorization Using Visual Proximity Coherence Optimization." IEEE Transactions on Cybernetics 48, no. 5 (May 2018): 1406–19. http://dx.doi.org/10.1109/tcyb.2017.2695655.

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22

Winer, E. H., and C. L. Bloebaum. "Visual design steering for optimization solution improvement." Structural and Multidisciplinary Optimization 22, no. 3 (October 2001): 219–29. http://dx.doi.org/10.1007/s001580100139.

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23

Kim, Gon-Woo, and Jae Byung Park. "2A1-G01 Uncalibrated Visual Servoing Technique with Obstacle Avoidance using Nonlinear Least Squares Optimization." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2009 (2009): _2A1—G01_1—_2A1—G01_3. http://dx.doi.org/10.1299/jsmermd.2009._2a1-g01_1.

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24

Kubo, Fumio. "Optimization Design tools Genesis, Visual DOC and Benchmark Examples." Proceedings of The Computational Mechanics Conference 2000.13 (2000): 789–90. http://dx.doi.org/10.1299/jsmecmd.2000.13.789.

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25

Park, Jun-su, and Soon-Yong Park. "Visual Inertial Navigation System Optimization using The Depth of Features with Stereo." Journal of the Institute of Electronics and Information Engineers 58, no. 7 (July 31, 2021): 77–87. http://dx.doi.org/10.5573/ieie.2021.58.7.77.

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26

Galkin, T. P., A. P. Nelubin, A. A. Galaev, D. D. Popov, V. V. Pilyugin, and S. Yu Misyurin. "Visual analytics in the case of multicriteria optimization." Scientific Visualization 10, no. 2 (July 2, 2018): 29–47. http://dx.doi.org/10.26583/sv.10.2.03.

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27

Ma, Bai, Wang, and Fang. "Robust Stereo Visual-Inertial Odometry Using Nonlinear Optimization." Sensors 19, no. 17 (August 29, 2019): 3747. http://dx.doi.org/10.3390/s19173747.

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Abstract (sommario):
The fusion of visual and inertial odometry has matured greatly due to the complementarity of the two sensors. However, the use of high-quality sensors and powerful processors in some applications is difficult due to size and cost limitations, and there are also many challenges in terms of robustness of the algorithm and computational efficiency. In this work, we present VIO-Stereo, a stereo visual-inertial odometry (VIO), which jointly combines the measurements of the stereo cameras and an inexpensive inertial measurement unit (IMU). We use nonlinear optimization to integrate visual measuremen
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28

Zhang Yi, 张易, 项志宇 Xiang Zhiyu, 陈舒雅 Chen Shuya, and 顾淑霞 Gu Shuxia. "Optimization on Visual Odometry under Weak Texture Environment." Acta Optica Sinica 38, no. 6 (2018): 0615001. http://dx.doi.org/10.3788/aos201838.0615001.

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29

Zhang, Lu, Tao Mei, Yuan Liu, Dacheng Tao, and He-Qin Zhou. "Visual search reranking via adaptive particle swarm optimization." Pattern Recognition 44, no. 8 (August 2011): 1811–20. http://dx.doi.org/10.1016/j.patcog.2011.01.016.

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30

Zhang, Baochang, Alessandro Perina, Ce Li, Qixiang Ye, Vittorio Murino, and Alessio Del Bue. "Manifold constraint transfer for visual structure-driven optimization." Pattern Recognition 77 (May 2018): 87–98. http://dx.doi.org/10.1016/j.patcog.2017.11.006.

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31

Karanjia, Rustum, Donald G. Brunet, and Martin W. ten Hove. "Optimization of Visual Evoked Potential (VEP) Recording Systems." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 36, no. 1 (January 2009): 89–92. http://dx.doi.org/10.1017/s0317167100006375.

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Abstract (sommario):
Objective:To explore the influence of environmental conditions on pattern visual evoked potential (VEP) recordings.Methods:Fourteen subjects with no known ocular pathology were recruited for the study. In an attempt to optimize the recording conditions, VEP recordings were performed in both the seated and recumbent positions. Comparisons were made between recordings using either LCD or CRT displays and recordings obtained in silence or with quiet background music. Paired recordings (in which only one variable was changed) were analyzed for changes in P100 latency, RMS noise, and variability.Re
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32

Murray, David W., and Bernard F. Buxton. "Scene Segmentation from Visual Motion Using Global Optimization." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-9, no. 2 (March 1987): 220–28. http://dx.doi.org/10.1109/tpami.1987.4767896.

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33

Patricio, Miguel A., Ivan Dotu, Jesús García, Antonio Berlanga, and José M. Molina. "DISCRETE OPTIMIZATION ALGORITHMS IN REAL-TIME VISUAL TRACKING." Applied Artificial Intelligence 23, no. 9 (October 22, 2009): 805–27. http://dx.doi.org/10.1080/08839510903235354.

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34

Fan, Xiaopeng, Feng Wu, Debin Zhao, and Oscar C. Au. "Distributed Wireless Visual Communication With Power Distortion Optimization." IEEE Transactions on Circuits and Systems for Video Technology 23, no. 6 (June 2013): 1040–53. http://dx.doi.org/10.1109/tcsvt.2013.2249019.

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35

Kardar, M., and A. Zee. "Information optimization in coupled audio-visual cortical maps." Proceedings of the National Academy of Sciences 99, no. 25 (November 21, 2002): 15894–97. http://dx.doi.org/10.1073/pnas.252472699.

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36

Yingcai Wu, Ka-Kei Chung, Huamin Qu, Xiaoru Yuan, and S. C. Cheung. "Interactive Visual Optimization and Analysis for RFID Benchmarking." IEEE Transactions on Visualization and Computer Graphics 15, no. 6 (November 2009): 1335–42. http://dx.doi.org/10.1109/tvcg.2009.156.

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37

Leutenegger, Stefan, Simon Lynen, Michael Bosse, Roland Siegwart, and Paul Furgale. "Keyframe-based visual–inertial odometry using nonlinear optimization." International Journal of Robotics Research 34, no. 3 (December 15, 2014): 314–34. http://dx.doi.org/10.1177/0278364914554813.

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38

Wen, Jinghuan, Huimin Ma, and Xiaoqin Zhang. "Optimization of the occlusion strategy in visual tracking." Tsinghua Science and Technology 21, no. 2 (April 2016): 221–30. http://dx.doi.org/10.1109/tst.2016.7442504.

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39

Morye, Akshay A., Chong Ding, Amit K. Roy-Chowdhury, and Jay A. Farrell. "Distributed Constrained Optimization for Bayesian Opportunistic Visual Sensing." IEEE Transactions on Control Systems Technology 22, no. 6 (November 2014): 2302–18. http://dx.doi.org/10.1109/tcst.2014.2300416.

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40

Miura, Kanako, Koichi Hashimoto, Hikaru Inooka, Jacques A. Gangloff, and Michel F. de Mathelin. "Model-less visual servoing using modified simplex optimization." Artificial Life and Robotics 10, no. 2 (November 15, 2006): 131–35. http://dx.doi.org/10.1007/s10015-005-0374-4.

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41

Patel, Swapnil C., Stephen M. Smith, Alexander T. Kessler, and Alok A. Bhatt. "Imaging of the Primary Visual Pathway based on Visual Deficits." Journal of Clinical Imaging Science 11 (April 7, 2021): 19. http://dx.doi.org/10.25259/jcis_12_2021.

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Abstract (sommario):
Vision loss can occur due to a variety of etiologies along the primary visual pathway. Understanding the anatomic organization of the visual pathway, which spans the globe to the occipital cortex, can help tailor neuroimaging to identify the cause of visual dysfunction. In this review, relevant anatomy and optimization of computed tomography and magnetic resonance imaging techniques will be described. This will be followed by a discussion of imaging findings related to pathologies at each functional anatomic level.
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42

Nenavath, Hathiram, and Ravi Kumar Jatoth. "Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking." Neural Computing and Applications 31, no. 9 (March 13, 2018): 5497–526. http://dx.doi.org/10.1007/s00521-018-3376-6.

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43

Zeng, Fanyu, and Chen Wang. "Visual Navigation with Asynchronous Proximal Policy Optimization in Artificial Agents." Journal of Robotics 2020 (October 14, 2020): 1–7. http://dx.doi.org/10.1155/2020/8702962.

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Vanilla policy gradient methods suffer from high variance, leading to unstable policies during training, where the policy’s performance fluctuates drastically between iterations. To address this issue, we analyze the policy optimization process of the navigation method based on deep reinforcement learning (DRL) that uses asynchronous gradient descent for optimization. A variant navigation (asynchronous proximal policy optimization navigation, appoNav) is presented that can guarantee the policy monotonic improvement during the process of policy optimization. Our experiments are tested in DeepMi
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44

Qin, Ru Ming, and Shi Ying Li. "Design and Realization of the Optimization System for ZLZ/ZLY Reducer." Applied Mechanics and Materials 215-216 (November 2012): 150–54. http://dx.doi.org/10.4028/www.scientific.net/amm.215-216.150.

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Based on the platform of Visual Basic6.0, Visual FoxPro6.0 and Visual LISP, the optimal design system is developed by establishing mathematical model for optimization design of ZLZ/ZLY reducer and using integer optimal method and visual combination software development technology. The system consists of several exe executable programs and lsp programs. Under the control of internal interface program, the system can realize multi-objective integer optimization design and gear parts drawing.
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45

Perez-Cisneros, Marco, Gerardo Garcia-Gil, Sabrina Vega-Maldonado, J. Arámburo-Lizárraga, Erik Cuevas, and Daniel Zaldivar. "Applying BAT Evolutionary Optimization to Image-Based Visual Servoing." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/590138.

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This paper presents a predictive control strategy for an image-based visual servoing scheme that employs evolutionary optimization. The visual control task is approached as a nonlinear optimization problem that naturally handles relevant visual servoing constraints such as workspace limitations and visibility restrictions. As the predictive scheme requires a reliable model, this paper uses a local model that is based on the visual interaction matrix and a global model that employs 3D trajectory data extracted from a quaternion-based interpolator. The work assumes a free-flying camera with 6-DO
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46

Beigie, Darin. "Solving Optimization Problems with Spreadsheets." Mathematics Teacher 111, no. 1 (September 2017): 26–33. http://dx.doi.org/10.5951/mathteacher.111.1.0026.

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47

Yoon, Sung-Joo, and Taejung Kim. "Development of Stereo Visual Odometry Based on Photogrammetric Feature Optimization." Remote Sensing 11, no. 1 (January 1, 2019): 67. http://dx.doi.org/10.3390/rs11010067.

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One of the important image processing technologies is visual odometry (VO) technology. VO estimates platform motion through a sequence of images. VO is of interest in the virtual reality (VR) industry as well as the automobile industry because the construction cost is low. In this study, we developed stereo visual odometry (SVO) based on photogrammetric geometric interpretation. The proposed method performed feature optimization and pose estimation through photogrammetric bundle adjustment. After corresponding the point extraction step, the feature optimization was carried out with photogramme
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48

Reddy, K. Narsimha, and Polaiah Bojja. "A new hybrid optimization method combining moth–flame optimization and teaching–learning-based optimization algorithms for visual tracking." Soft Computing 24, no. 24 (May 26, 2020): 18321–47. http://dx.doi.org/10.1007/s00500-020-05032-1.

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49

Hamroun, Mohamed, Sonia Lajmi, Henri Nicolas, and Ikram Amous. "Descriptor Optimization for Semantic Concept Detection Using Visual Content." International Journal of Strategic Information Technology and Applications 10, no. 1 (January 2019): 40–59. http://dx.doi.org/10.4018/ijsita.2019010103.

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Abstract (sommario):
Concept detection has been considered a difficult problem and has attracted the interest of the content-based multimedia retrieval community. This detection implies an association between the concept and the visual content. In other words, the visual characteristics extracted from the video. This includes taking knowledge about the concept itself and its context. This work focuses on the problem of concept detection. For that, several stages are elaborated: first, a method of extraction and semi-automatic annotation of the video plans for the training set is proposed. This new method is based
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50

Ji, Xun, Qidan Zhu, Junda Ma, Peng Lu, and Tianhao Yan. "Three Landmark Optimization Strategies for Mobile Robot Visual Homing." Sensors 18, no. 10 (September 20, 2018): 3180. http://dx.doi.org/10.3390/s18103180.

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Visual homing is an attractive autonomous mobile robot navigation technique, which only uses vision sensors to guide the robot to the specified target location. Landmark is the only input form of the visual homing approaches, which is usually represented by scale-invariant features. However, the landmark distribution has a great impact on the homing performance of the robot, as irregularly distributed landmarks will significantly reduce the navigation precision. In this paper, we propose three strategies to solve this problem. We use scale-invariant feature transform (SIFT) features as natural
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