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Artículos de revistas sobre el tema "Visual optimization"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 10 de febrero de 2022

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1

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

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2

Guo, Fei, Yuan Yang y 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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3

Gepshtein, Sergei y Thomas D. Albright. "Adaptive Optimization of Visual Sensitivity". Journal of the Indian Institute of Science 97, n.º 4 (25 de noviembre de 2017): 423–34. http://dx.doi.org/10.1007/s41745-017-0056-y.

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4

Sivertsen, Edvard, Frøydis Bjerke, Trygve Almøy, Vegard Segtnan y Tormod Næs. "Multivariate optimization by visual inspection". Chemometrics and Intelligent Laboratory Systems 85, n.º 1 (enero de 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, n.º 8 (1 de octubre de 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, n.º 14 (julio de 1997): 1939–51. http://dx.doi.org/10.1016/s0042-6989(97)00002-3.

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7

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

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8

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

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9

Torreão, JoséR A. y Edward Roe. "Microcanonical optimization applied to visual processing". Physics Letters A 205, n.º 5-6 (septiembre de 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 y and Xu Liu and Xu Liu. "Optimization algorithm of near-eye light field displays based on human visual characteristics". Chinese Optics Letters 14, n.º 4 (2016): 041101–41105. http://dx.doi.org/10.3788/col201614.041101.

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11

Osipov, Vasily Yurevich y Victor Victorovich Kuzenny. "Optimization of visual environment for spatial modeling". SPIIRAS Proceedings 1, n.º 12 (17 de marzo de 2014): 235. http://dx.doi.org/10.15622/sp.12.16.

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12

Vasiliev, Petr Victorovich y Victor Victorovich Kuzenny. "Optimization of visual environment for spatial modeling". SPIIRAS Proceedings 4, n.º 19 (17 de marzo de 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 y Yangyan Li. "Group optimization for multi-attribute visual embedding". Visual Informatics 2, n.º 3 (septiembre de 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 y D. S. Fukalov. "Visual quality optimization for real-time encoders". Pattern Recognition and Image Analysis 20, n.º 2 (junio de 2010): 185–91. http://dx.doi.org/10.1134/s1054661810020100.

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15

Choi, K. H. y J. N. Hwang. "Constrained Optimization for Audio-to-Visual Conversion". IEEE Transactions on Signal Processing 52, n.º 6 (junio de 2004): 1783–90. http://dx.doi.org/10.1109/tsp.2004.827153.

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16

Koushik, Jayanth, Austin Marcus, Aarti Singh y Michael Tarr. "Real-time Optimization for Visual Feature Identification". Journal of Vision 18, n.º 10 (1 de septiembre de 2018): 413. http://dx.doi.org/10.1167/18.10.413.

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17

Wang, Wei, Zhaoming Chen y Mingquan Shi. "Visual tracking with complementary deep feature optimization". Journal of Electronic Imaging 27, n.º 04 (27 de agosto de 2018): 1. http://dx.doi.org/10.1117/1.jei.27.4.043052.

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18

Jia Li, Yonghong Tian, Lingyu Duan y Tiejun Huang. "Estimating Visual Saliency Through Single Image Optimization". IEEE Signal Processing Letters 20, n.º 9 (septiembre de 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 y Angelo Cenedese. "Cooperative Optimization of UAVs Formation Visual Tracking". Robotics 8, n.º 3 (7 de julio de 2019): 52. http://dx.doi.org/10.3390/robotics8030052.

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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, namely the visual tracking of an evader (target) by means of a fleet of autonomous aerial vehicles, with the specific aim of focusing on the target so as to perform an accurate position estimation while concurrently allowing a wide coverage over the monitored area so as to limit the probability of losing the target itself. These clearly conflicting objectives call for an optimization approach that is here developed: by considering both aforementioned aspects and the cooperative capabilities of the fleet, the designed algorithm allows controling in real time the single fields of view so as to counteract evasion maneuvers and maximize an overall performance index. The proposed strategy is discussed and finally assessed through the realistic Gazebo-ROS simulation framework.

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20

Nguyen, Giang Phuong y Marcel Worring. "Optimization of interactive visual-similarity-based search". ACM Transactions on Multimedia Computing, Communications, and Applications 4, n.º 1 (enero de 2008): 1–23. http://dx.doi.org/10.1145/1324287.1324294.

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21

Tao, Yizhang, Yiyi Shen, Bin Sheng, Ping Li y Rynson W. H. Lau. "Video Decolorization Using Visual Proximity Coherence Optimization". IEEE Transactions on Cybernetics 48, n.º 5 (mayo de 2018): 1406–19. http://dx.doi.org/10.1109/tcyb.2017.2695655.

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22

Winer, E. H. y C. L. Bloebaum. "Visual design steering for optimization solution improvement". Structural and Multidisciplinary Optimization 22, n.º 3 (octubre de 2001): 219–29. http://dx.doi.org/10.1007/s001580100139.

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23

Kim, Gon-Woo y 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 y 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, n.º 7 (31 de julio de 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 y S. Yu Misyurin. "Visual analytics in the case of multicriteria optimization". Scientific Visualization 10, n.º 2 (2 de julio de 2018): 29–47. http://dx.doi.org/10.26583/sv.10.2.03.

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27

Ma, Bai, Wang y Fang. "Robust Stereo Visual-Inertial Odometry Using Nonlinear Optimization". Sensors 19, n.º 17 (29 de agosto de 2019): 3747. http://dx.doi.org/10.3390/s19173747.

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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 measurements with IMU readings in VIO tightly. To decrease the cost of computation, we use the FAST feature detector to improve its efficiency and track features by the KLT sparse optical flow algorithm. We also incorporate accelerometer bias into the measurement model and optimize it together with other variables. Additionally, we perform circular matching between the previous and current stereo image pairs in order to remove outliers in the stereo matching and feature tracking steps, thus reducing the mismatch of feature points and improving the robustness and accuracy of the system. Finally, this work contributes to the experimental comparison of monocular visual-inertial odometry and stereo visual-inertial odometry by evaluating our method using the public EuRoC dataset. Experimental results demonstrate that our method exhibits competitive performance with the most advanced techniques.

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28

Zhang Yi, 张易, 项志宇 Xiang Zhiyu, 陈舒雅 Chen Shuya y 顾淑霞 Gu Shuxia. "Optimization on Visual Odometry under Weak Texture Environment". Acta Optica Sinica 38, n.º 6 (2018): 0615001. http://dx.doi.org/10.3788/aos201838.0615001.

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29

Zhang, Lu, Tao Mei, Yuan Liu, Dacheng Tao y He-Qin Zhou. "Visual search reranking via adaptive particle swarm optimization". Pattern Recognition 44, n.º 8 (agosto de 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 y Alessio Del Bue. "Manifold constraint transfer for visual structure-driven optimization". Pattern Recognition 77 (mayo de 2018): 87–98. http://dx.doi.org/10.1016/j.patcog.2017.11.006.

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31

Karanjia, Rustum, Donald G. Brunet y Martin W. ten Hove. "Optimization of Visual Evoked Potential (VEP) Recording Systems". Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 36, n.º 1 (enero de 2009): 89–92. http://dx.doi.org/10.1017/s0317167100006375.

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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.Results:Baseline RMS noise demonstrated a significant decrease in the variability during the first 50msec accompanied by a 73% decrease in recording time for recumbent position when compared to the seated position (p<0.05). Visual evoked potentials recorded using LCD monitors demonstrated a significant increase in the P100 latency when compared to CRT recordings in the same subjects. The addition of background music did not affect the amount of RMS noise during the first 50msec of the recordings.Conclusion:This study demonstrates that the use of the recumbent position increases patient comfort and improves the signal to noise ratio. In contrast, the addition of background music to relax the patient did not improve the recording signal. Furthermore, the study illustrates the importance of avoiding low-contrast visual stimulation patterns obtained with LCD as they lead to higher latencies resulting in false positive recordings. These findings are important when establishing or modifying a pattern VEP recording protocol.

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32

Murray, David W. y Bernard F. Buxton. "Scene Segmentation from Visual Motion Using Global Optimization". IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-9, n.º 2 (marzo de 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 y José M. Molina. "DISCRETE OPTIMIZATION ALGORITHMS IN REAL-TIME VISUAL TRACKING". Applied Artificial Intelligence 23, n.º 9 (22 de octubre de 2009): 805–27. http://dx.doi.org/10.1080/08839510903235354.

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34

Fan, Xiaopeng, Feng Wu, Debin Zhao y Oscar C. Au. "Distributed Wireless Visual Communication With Power Distortion Optimization". IEEE Transactions on Circuits and Systems for Video Technology 23, n.º 6 (junio de 2013): 1040–53. http://dx.doi.org/10.1109/tcsvt.2013.2249019.

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35

Kardar, M. y A. Zee. "Information optimization in coupled audio-visual cortical maps". Proceedings of the National Academy of Sciences 99, n.º 25 (21 de noviembre de 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 y S. C. Cheung. "Interactive Visual Optimization and Analysis for RFID Benchmarking". IEEE Transactions on Visualization and Computer Graphics 15, n.º 6 (noviembre de 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 y Paul Furgale. "Keyframe-based visual–inertial odometry using nonlinear optimization". International Journal of Robotics Research 34, n.º 3 (15 de diciembre de 2014): 314–34. http://dx.doi.org/10.1177/0278364914554813.

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38

Wen, Jinghuan, Huimin Ma y Xiaoqin Zhang. "Optimization of the occlusion strategy in visual tracking". Tsinghua Science and Technology 21, n.º 2 (abril de 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 y Jay A. Farrell. "Distributed Constrained Optimization for Bayesian Opportunistic Visual Sensing". IEEE Transactions on Control Systems Technology 22, n.º 6 (noviembre de 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 y Michel F. de Mathelin. "Model-less visual servoing using modified simplex optimization". Artificial Life and Robotics 10, n.º 2 (15 de noviembre de 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 y Alok A. Bhatt. "Imaging of the Primary Visual Pathway based on Visual Deficits". Journal of Clinical Imaging Science 11 (7 de abril de 2021): 19. http://dx.doi.org/10.25259/jcis_12_2021.

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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 y Ravi Kumar Jatoth. "Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking". Neural Computing and Applications 31, n.º 9 (13 de marzo de 2018): 5497–526. http://dx.doi.org/10.1007/s00521-018-3376-6.

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43

Zeng, Fanyu y Chen Wang. "Visual Navigation with Asynchronous Proximal Policy Optimization in Artificial Agents". Journal of Robotics 2020 (14 de octubre de 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 DeepMind Lab, and the experimental results show that the artificial agents with appoNav perform better than the compared algorithm.

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44

Qin, Ru Ming y Shi Ying Li. "Design and Realization of the Optimization System for ZLZ/ZLY Reducer". Applied Mechanics and Materials 215-216 (noviembre de 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 y 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-DOF simulation whose results support the discussion on the constraint handling and the image prediction scheme.

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46

Beigie, Darin. "Solving Optimization Problems with Spreadsheets". Mathematics Teacher 111, n.º 1 (septiembre de 2017): 26–33. http://dx.doi.org/10.5951/mathteacher.111.1.0026.

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47

Yoon, Sung-Joo y Taejung Kim. "Development of Stereo Visual Odometry Based on Photogrammetric Feature Optimization". Remote Sensing 11, n.º 1 (1 de enero de 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 photogrammetry-based and vision-based optimization. Then, absolute orientation was performed for pose estimation through bundle adjustment. We used ten sequences provided by the Karlsruhe institute of technology and Toyota technological institute (KITTI) community. Through a two-step optimization process, we confirmed that the outliers, which were not removed by conventional outlier filters, were removed. We also were able to confirm the applicability of photogrammetric techniques to stereo visual odometry technology.

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48

Reddy, K. Narsimha y Polaiah Bojja. "A new hybrid optimization method combining moth–flame optimization and teaching–learning-based optimization algorithms for visual tracking". Soft Computing 24, n.º 24 (26 de mayo de 2020): 18321–47. http://dx.doi.org/10.1007/s00500-020-05032-1.

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49

Hamroun, Mohamed, Sonia Lajmi, Henri Nicolas y Ikram Amous. "Descriptor Optimization for Semantic Concept Detection Using Visual Content". International Journal of Strategic Information Technology and Applications 10, n.º 1 (enero de 2019): 40–59. http://dx.doi.org/10.4018/ijsita.2019010103.

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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 on the genetic algorithm. Then, a preliminary concept detection is carried out to generate the visual dictionary (BoVS). This second step is improved thanks to a noise reduction mechanism. This article's contribution has proven its effectiveness by testing it on a large dataset.

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50

Ji, Xun, Qidan Zhu, Junda Ma, Peng Lu y Tianhao Yan. "Three Landmark Optimization Strategies for Mobile Robot Visual Homing". Sensors 18, n.º 10 (20 de septiembre de 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 landmarks, and the proposed strategies can optimize the landmark distribution without over-eliminating landmarks or increasing calculation amount. Experiments on both panoramic image databases and a real mobile robot have verified the effectiveness and feasibility of the proposed strategies.

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