Bibliografías temáticas / Depth of field fusion

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Literatura académica sobre el tema "Depth of field fusion"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Depth of field fusion"

1

汪, 嘉欣. "A Fast Multi-Exposure Fusion Algorithm for Ultra Depth of Field Fusion." Modeling and Simulation 13, no. 03 (2024): 3797–806. http://dx.doi.org/10.12677/mos.2024.133346.

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Chen, Zhaoyu, Qiankun Liu, Ke Xu, and Xiaoyang Liu. "Weighted Fusion Method of Marine Gravity Field Model Based on Water Depth Segmentation." Remote Sensing 16, no. 21 (2024): 4107. http://dx.doi.org/10.3390/rs16214107.

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Among the marine gravity field models derived from satellite altimetry, the Scripps Institution of Oceanography (SIO) series and Denmark Technical University (DTU) series models are the most representative and are often used to integrate global gravity field models, which were inverted by the deflection of vertical method and sea surface height method, respectively. The fusion method based on the offshore distance used in the EGM2008 model is just model stitching, which cannot realize the true fusion of the two types of marine gravity field models. In the paper, a new fusion method based on wa
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3

Wang, Shuzhen, Haili Zhao, and Wenbo Jing. "Fast all-focus image reconstruction method based on light field imaging." ITM Web of Conferences 45 (2022): 01030. http://dx.doi.org/10.1051/itmconf/20224501030.

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To achieve high-quality imaging of all focal planes with large depth of field information, a fast all-focus image reconstruction technology based on light field imaging is proposed: combining light field imaging to collect field of view information, and using light field reconstruction to obtain a multi-focus image source set, using the improved NSML image fusion method performs image fusion to quickly obtain an all-focus image with a large depth of field. Experiments have proved that this method greatly reduces the time consumed in the image fusion process by simplifying the calculation proce
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Chen, Jiaxin, Shuo Zhang, and Youfang Lin. "Attention-based Multi-Level Fusion Network for Light Field Depth Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1009–17. http://dx.doi.org/10.1609/aaai.v35i2.16185.

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Depth estimation from Light Field (LF) images is a crucial basis for LF related applications. Since multiple views with abundant information are available, how to effectively fuse features of these views is a key point for accurate LF depth estimation. In this paper, we propose a novel attention-based multi-level fusion network. Combined with the four-branch structure, we design intra-branch fusion strategy and inter-branch fusion strategy to hierarchically fuse effective features from different views. By introducing the attention mechanism, features of views with less occlusions and richer te
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Piao, Yongri, Miao Zhang, Xiaohui Wang, and Peihua Li. "Extended depth of field integral imaging using multi-focus fusion." Optics Communications 411 (March 2018): 8–14. http://dx.doi.org/10.1016/j.optcom.2017.10.081.

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Fu, Bangshao, Xunbo Yu, Xin Gao, et al. "Depth-of-field enhancement in light field display based on fusion of voxel information on the depth plane." Optics and Lasers in Engineering 183 (December 2024): 108543. http://dx.doi.org/10.1016/j.optlaseng.2024.108543.

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Oucherif, Sabrine Djedjiga, Mohamad Motasem Nawaf, Jean-Marc Boï, et al. "Enhancing Facial Expression Recognition through Light Field Cameras." Sensors 24, no. 17 (2024): 5724. http://dx.doi.org/10.3390/s24175724.

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In this paper, we study facial expression recognition (FER) using three modalities obtained from a light field camera: sub-aperture (SA), depth map, and all-in-focus (AiF) images. Our objective is to construct a more comprehensive and effective FER system by investigating multimodal fusion strategies. For this purpose, we employ EfficientNetV2-S, pre-trained on AffectNet, as our primary convolutional neural network. This model, combined with a BiGRU, is used to process SA images. We evaluate various fusion techniques at both decision and feature levels to assess their effectiveness in enhancin
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Pu, Can, Runzi Song, Radim Tylecek, Nanbo Li, and Robert Fisher. "SDF-MAN: Semi-Supervised Disparity Fusion with Multi-Scale Adversarial Networks." Remote Sensing 11, no. 5 (2019): 487. http://dx.doi.org/10.3390/rs11050487.

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Refining raw disparity maps from different algorithms to exploit their complementary advantages is still challenging. Uncertainty estimation and complex disparity relationships among pixels limit the accuracy and robustness of existing methods and there is no standard method for fusion of different kinds of depth data. In this paper, we introduce a new method to fuse disparity maps from different sources, while incorporating supplementary information (intensity, gradient, etc.) into a refiner network to better refine raw disparity inputs. A discriminator network classifies disparities at diffe
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Gao, Yuxuan, Haiwei Zhang, Zhihong Chen, Lifang Xue, Yinping Miao, and Jiamin Fu. "Enhanced light field depth estimation through occlusion refinement and feature fusion." Optics and Lasers in Engineering 184 (January 2025): 108655. http://dx.doi.org/10.1016/j.optlaseng.2024.108655.

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De, Ishita, Bhabatosh Chanda, and Buddhajyoti Chattopadhyay. "Enhancing effective depth-of-field by image fusion using mathematical morphology." Image and Vision Computing 24, no. 12 (2006): 1278–87. http://dx.doi.org/10.1016/j.imavis.2006.04.005.

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Tesis sobre el tema "Depth of field fusion"

1

Duan, Jun Wei. "New regional multifocus image fusion techniques for extending depth of field." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3951602.

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Hua, Xiaoben, and Yuxia Yang. "A Fusion Model For Enhancement of Range Images." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2203.

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In this thesis, we would like to present a new way to enhance the “depth map” image which is called as the fusion of depth images. The goal of our thesis is to try to enhance the “depth images” through a fusion of different classification methods. For that, we will use three similar but different methodologies, the Graph-Cut, Super-Pixel and Principal Component Analysis algorithms to solve the enhancement and output of our result. After that, we will compare the effect of the enhancement of our result with the original depth images. This result indicates the effectiveness of our methodology.<b
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Ocampo, Blandon Cristian Felipe. "Patch-Based image fusion for computational photography." Electronic Thesis or Diss., Paris, ENST, 2018. http://www.theses.fr/2018ENST0020.

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Dans de nombreuses situations, la dynamique des capteurs ou la profondeur de champ des appareils photographiques conventionnels sont insuffisantes pour capturer fidèlement des scènes naturelles. Une méthode classique pour contourner ces limitations est de fusionner des images acquises avec des paramètres de prise de vue variables. Ces méthodes nécessitent que les images soient parfaitement alignées et que les scènes soient statiques, faute de quoi des artefacts (fantômes) ou des structures irrégulières apparaissent lors de la fusion. Le but de cette thèse est de développer des techniques perme
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Ramirez, Hernandez Pavel. "Extended depth of field." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/9941.

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In this thesis the extension of the depth of field of optical systems is investigated. The problem of achieving extended depth of field (EDF) while preserving the transverse resolution is also addressed. A new expression for the transport of intensity equation in the prolate spheroidal coordinates system is derived, with the aim of investigating the phase retrieval problem with applications to EDF. A framework for the optimisation of optical systems with EDF is also introduced, where the main motivation is to find an appropriate scenario that will allow a convex optimisation solution leading t
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Sikdar, Ankita. "Depth based Sensor Fusion in Object Detection and Tracking." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1515075130647622.

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Villarruel, Christina R. "Computer graphics and human depth perception with gaze-contingent depth of field /." Connect to online version, 2006. http://ada.mtholyoke.edu/setr/websrc/pdfs/www/2006/175.pdf.

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Aldrovandi, Lorenzo. "Depth estimation algorithm for light field data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Botcherby, Edward J. "Aberration free extended depth of field microscopy." Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:7ad8bc83-6740-459f-8c48-76b048c89978.

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In recent years, the confocal and two photon microscopes have become ubiquitous tools in life science laboratories. The reason for this is that both these systems can acquire three dimensional image data from biological specimens. Specifically, this is done by acquiring a series of two-dimensional images from a set of equally spaced planes within the specimen. The resulting image stack can be manipulated and displayed on a computer to reveal a wealth of information. These systems can also be used in time lapse studies to monitor the dynamical behaviour of specimens by recording a number of ima
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Möckelind, Christoffer. "Improving deep monocular depth predictions using dense narrow field of view depth images." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235660.

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In this work we study a depth prediction problem where we provide a narrow field of view depth image and a wide field of view RGB image to a deep network tasked with predicting the depth for the entire RGB image. We show that by providing a narrow field of view depth image, we improve results for the area outside the provided depth compared to an earlier approach only utilizing a single RGB image for depth prediction. We also show that larger depth maps provide a greater advantage than smaller ones and that the accuracy of the model decreases with the distance from the provided depth. Further,
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Luraas, Knut. "Clinical aspects of Critical Flicker Fusion perimetry : an in-depth analysis." Thesis, Cardiff University, 2012. http://orca.cf.ac.uk/39684/.

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The thesis evaluated, in three studies, the clinical potential of Critical Flicker Fusion perimetry (CFFP) undertaken using the Octopus 311 perimeter. The influence of the learning effect on the outcome of CFFP was evaluated, in each eye at each of five visits each separated by one week, for 28 normal individuals naïve to perimetry, 10 individuals with ocular hypertension (OHT) and 11 with open angle glaucoma (OAG) all of whom were experienced in Standard Automated perimetry (SAP). An improvement occurred in the height, rather than in the shape, of the visual field and was largest for those wi
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Libros sobre el tema "Depth of field fusion"

1

Heyen, William. Depth of field: Poems. Carnegie Mellon University Press, 2005.

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1944-, Slattery Dennis Patrick, and Corbett Lionel, eds. Depth psychology: Meditations from the field. Daimon, 2000.

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Donal, Cooper, Leino Marika, Henry Moore Institute (Leeds, England), and Victoria and Albert Museum, eds. Depth of field: Relief sculpture in Renaissance Italy. Peter Lang, 2007.

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Buch, Neeraj. Precast concrete panel systems for full-depth pavement repairs: Field trials. Office of Infrastructure, Office of Pavement Technology, Federal Highway Administration, U.S. Department of Transportation, 2007.

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Geoffrey, Cocks, Diedrick James, and Perusek Glenn, eds. Depth of field: Stanley Kubrick, film, and the uses of history. University of Wisconsin Press, 2006.

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6

Ruotoistenmäki, Tapio. Estimation of depth to potential field sources using the Fourier amplitude spectrum. Geologian tutkimuskeskus, 1987.

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Goss, Keith Michael. Multi-dimensional polygon-based rendering for motion blur and depth of field. Brunel University, 1986.

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Jose, Peñaranda Victor, Photobank Philippines Inc, and NCCP/Human Rights Desk, eds. Depth of field: Photographs of poverty, repression, and struggle in the Philippines. Photobank Philippines, 1987.

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9

Rantamäki, Karin. Particle-in-cell simulations of the near-field of a lower hybrid grill. VTT Technical Research Centre of Finland, 2003.

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Timmins 2003 Field Conference Ore Deposits at Depth 2003. Timmins 2003 Field Conference: Ore deposits at depth challenges and opportunities, Technical sessions abstract volume and field trip guide, CIM. Canadian Institute of Mining and Metallurgy, 2003.

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Capítulos de libros sobre el tema "Depth of field fusion"

1

Zhang, Yukun, Yongri Piao, Xinxin Ji, and Miao Zhang. "Dynamic Fusion Network for Light Field Depth Estimation." In Pattern Recognition and Computer Vision. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88007-1_1.

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Liu, Xinshi, Dongmei Fu, Chunhong Wu, and Ze Si. "The Depth Estimation Method Based on Double-Cues Fusion for Light Field Images." In Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019). Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0474-7_67.

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Kexun, Zheng, Gan Feifei, Zhao Daiyao, Chen Xiao, Liu Xianggang, and Zhang Ning. "Research on Identification of Deep Leakage Channels in Karst Pumped Storage Reservoirs Based on Multi Field Data Fusion." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-9184-2_21.

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AbstractThe most prominent engineering geological problem of pumped storage power station reservoirs in karst areas is karst leakage, the development of karst leakage channels has a significant impact on the selection of reservoir locations, layout of engineering buildings, design of anti-seepage measures, and engineering costs. Therefore, the survey and evaluation of reservoir leakage channels are the foundation for the construction of pumped storage power station reservoirs in karst areas. The field analysis method plays an important role in karst leakage survey. Traditional karst groundwate
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4

Gooch, Jan W. "Depth of Field." In Encyclopedic Dictionary of Polymers. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_3432.

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Atchison, David A., and George Smith. "Depth-of-Field." In Optics of the Human Eye, 2nd ed. CRC Press, 2023. http://dx.doi.org/10.1201/9781003128601-24.

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Kemp, Jonathan. "Depth of field." In Film on Video. Routledge, 2019. http://dx.doi.org/10.4324/9780429468872-6.

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Ravitz, Jeff, and James L. Moody. "Depth of Field." In Lighting for Televised Live Events. Routledge, 2021. http://dx.doi.org/10.4324/9780429288982-11.

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Cai, Ziyun, Yang Long, Xiao-Yuan Jing, and Ling Shao. "Adaptive Visual-Depth Fusion Transfer." In Computer Vision – ACCV 2018. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20870-7_4.

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Turaev, Vladimir, and Alexis Virelizier. "Fusion categories." In Monoidal Categories and Topological Field Theory. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49834-8_4.

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Blaker, Alfred A. "Enough Depth, Enough Sharpness." In Applied Depth of Field. Routledge, 2024. http://dx.doi.org/10.4324/9781003565192-1.

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Actas de conferencias sobre el tema "Depth of field fusion"

1

Ma, Jinshan, and Changxiang Wang. "Image dehazing algorithm of transmittance fusion based on light field depth information." In 4th International Conference on Signal Image Processing and Communication, edited by Xianye Ben and Lei Chen. SPIE, 2024. http://dx.doi.org/10.1117/12.3041001.

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Tan, Feng, Huiping Deng, Sen Xiang, and Jin Wu. "Light field depth estimation based on fusion of multi-scale semantic and geometric information." In 2024 IEEE International Conference on Visual Communications and Image Processing (VCIP). IEEE, 2024. https://doi.org/10.1109/vcip63160.2024.10849895.

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Qiao, Lingfeng, Guodong Liu, Cheng Lu, and Binghui Lu. "Research on Target Localization Technology based on Depth of field fusion of generative adversarial networks." In 2024 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2024. http://dx.doi.org/10.1109/icma61710.2024.10633036.

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Liu, Qiankun, Zhaoyu Chen, and Ke Xu. "Marine Gravity Field Model Fusion Method Based on Water Depth: A Case Study of the South China Sea." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642576.

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Tondo, Gledson Rodrigo, Guido Morgenthal, and Charles Riley. "Portable depth sensing with the iPhone time-of-flight LiDAR." In IABSE Congress, San José 2024: Beyond Structural Engineering in a Changing World. International Association for Bridge and Structural Engineering (IABSE), 2024. https://doi.org/10.2749/sanjose.2024.0937.

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&lt;p&gt;Modern smartphones offer diverse features, including ample storage, wireless data transfer, and various sensors, making them valuable for structural data collection. This study investigates the iPhone's LiDAR system for depth data collection, defining its field of view and assessing its performance for static and dynamic targets. We analyse limitations such as phone-to-target distance and noise properties. Measurement comparisons with a laser displacement transducer are conducted under different conditions to characterise the sensor’s properties. Discussions on the results include ins
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Paakkonen, Scott T., Samuel F. Lockwood, Daniel H. Pope, Valerie G. Horner, Edgar A. Morris, and Daniel P. Werner. "The Role of Coatings and Cathodic Protection in Microbiologically Influenced Corrosion." In CORROSION 1993. NACE International, 1993. https://doi.org/10.5006/c1993-93293.

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Abstract A field study was performed to assess the influence of local soil conditions (biological and chemical) on the ability to achieve "adequate" cathodic protection and reduce corrosion, including microbiologically influenced corrosion (MIC). The performance of four coatings was also assessed. Two sites were chosen based on inherent differences in soil corrosiveness. Site A was adjacent to a pipeline site at which MIC was implicated in the corrosion process leading to a failure. Site B was a site adjacent to the same pipeline (one-quarter mile from Site A) that had shown significantly less
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Chai, Ruimin, and Mengjiao Du. "Multiscale feature fusion-based monocular depth estimation." In 4th International Conference on Electronic Information Engineering and Data Processing (EIEDP 2025), edited by Azlan Bin Mohd Zain and Lei Chen. SPIE, 2025. https://doi.org/10.1117/12.3067068.

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Li, Han, Yukai Ma, Yaqing Gu, Kewei Hu, Yong Liu, and Xingxing Zuo. "RadarCam-Depth: Radar-Camera Fusion for Depth Estimation with Learned Metric Scale." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610929.

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Song, Tieshuai, Bin Yang, Jun Wang, Guidong He, Zhao Dong, and Fengjun Zhong. "mmWave Radar and Image Fusion for Depth Completion: a Two-Stage Fusion Network." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706480.

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Hilmarsen, Henrik, Nicholas Dalhaug, Trym Anthonsen Nygård, Edmund Førland Brekke, Rudolf Mester, and Annette Stahl. "Maritime Tracking-By-Detection with Object Mask Depth Retrieval Through Stereo Vision and Lidar*." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706307.

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Informes sobre el tema "Depth of field fusion"

1

McLean, William E. ANVIS Objective Lens Depth of Field. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada306571.

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Al-Mutawaly, Nafia, Hubert de Bruin, and Raymond D. Findlay. Magnetic Nerve Stimulation: Field Focality and Depth of Penetration. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada411028.

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Peng, Y. K. M. Spherical torus, compact fusion at low field. Office of Scientific and Technical Information (OSTI), 1985. http://dx.doi.org/10.2172/6040602.

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Paul, A. C., and V. K. Neil. Fixed Field Alternating Gradient recirculator for heavy ion fusion. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/5828376.

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Cathey, W. T., Benjamin Braker, and Sherif Sherif. Analysis and Design Tools for Passive Ranging and Reduced-Depth-of-Field Imaging. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada417814.

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G.J. Kramer, R. Nazikian, and and E. Valeo. Correlation Reflectometry for Turbulence and Magnetic Field Measurements in Fusion Plasmas. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/808282.

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Claycomb, William R., Roy Maxion, Jason Clark, et al. Deep Focus: Increasing User Depth of Field" to Improve Threat Detection (Oxford Workshop Poster)". Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada610980.

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Boots, Byron, Jacob Sacks, Kevin Choi, et al. Downhole Sensing and Event-Driven Sensor Fusion for Depth-of-Cut Based Autonomous Fault Response and Drilling Optimization. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2430188.

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Grabowski, Theodore C. Directed Energy HPM, PP, & PPS Efforts: Magnetized Target Fusion - Field Reversed Configuration. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada460910.

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Hasegawa, Akira, and Liu Chen. A D-He/sup 3/ fusion reactor based on a dipole magnetic field. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/5819503.

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