Zeitschriftenartikel: „Real-time correction“

1

Engelke, Robert M. „Real-time transcription correction system“. Journal of the Acoustical Society of America 114, Nr. 5 (2003): 2544. http://dx.doi.org/10.1121/1.1634110.

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2

Yu, L. H., E. Bozoki, J. Galayda, S. Krinsky und G. Vignola. „Real time harmonic closed orbit correction“. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 284, Nr. 2-3 (Dezember 1989): 268–85. http://dx.doi.org/10.1016/0168-9002(89)90292-1.

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3

Zhang, Kai. „Gctf: Real-time CTF determination and correction“. Journal of Structural Biology 193, Nr. 1 (Januar 2016): 1–12. http://dx.doi.org/10.1016/j.jsb.2015.11.003.

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4

van der Kouwe, André, und Anders Dale. „Real-time motion correction using octant navigators“. NeuroImage 13, Nr. 6 (Juni 2001): 48. http://dx.doi.org/10.1016/s1053-8119(01)91391-6.

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5

Rand, J., A. Hoover, S. Fishel, J. Moss, J. Pappas und E. Muth. „Real-Time Correction of Heart Interbeat Intervals“. IEEE Transactions on Biomedical Engineering 54, Nr. 5 (Mai 2007): 946–50. http://dx.doi.org/10.1109/tbme.2007.893491.

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6

Downie, John D. „Real-time holographic image correction using bacteriorhodopsin“. Applied Optics 33, Nr. 20 (10.07.1994): 4353. http://dx.doi.org/10.1364/ao.33.004353.

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7

Schops, Thomas, Martin R. Oswald, Pablo Speciale, Shuoran Yang und Marc Pollefeys. „Real-Time View Correction for Mobile Devices“. IEEE Transactions on Visualization and Computer Graphics 23, Nr. 11 (November 2017): 2455–62. http://dx.doi.org/10.1109/tvcg.2017.2734578.

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8

Wang, C.-Y., P. Elliott, S. Sharma und J. K. Dewhurst. „Real time scissor correction in TD-DFT“. Journal of Physics: Condensed Matter 31, Nr. 21 (19.03.2019): 214002. http://dx.doi.org/10.1088/1361-648x/ab048a.

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9

Glagolev, Vladislav, und Alexander Ladonkin. „Real-time perspective correction in video stream“. MATEC Web of Conferences 158 (2018): 01010. http://dx.doi.org/10.1051/matecconf/201815801010.

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The paper describes an algorithm used for software perspective correction. The algorithm uses the camera’s orientation angles and transforms the coordinates of pixels on a source image to coordinates on a virtual image form the camera whose focal plane is perpendicular to the gravity vector. This algorithm can be used as a low-cost replacement of a gyrostabilazer in specific applications that restrict using movable parts or heavy and pricey equipment.

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10

Asensio Ramos, A., J. de la Cruz Rodríguez und A. Pastor Yabar. „Real-time, multiframe, blind deconvolution of solar images“. Astronomy & Astrophysics 620 (Dezember 2018): A73. http://dx.doi.org/10.1051/0004-6361/201833648.

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The quality of images of the Sun obtained from the ground are severely limited by the perturbing effect of the Earth’s turbulent atmosphere. The post-facto correction of the images to compensate for the presence of the atmosphere require the combination of high-order adaptive optics techniques, fast measurements to freeze the turbulent atmosphere, and very time-consuming blind deconvolution algorithms. Under mild seeing conditions, blind deconvolution algorithms can produce images of astonishing quality. They can be very competitive with those obtained from space, with the huge advantage of the flexibility of the instrumentation thanks to the direct access to the telescope. In this contribution we make use of deep learning techniques to significantly accelerate the blind deconvolution process and produce corrected images at a peak rate of ∼100 images per second. We present two different architectures that produce excellent image corrections with noise suppression while maintaining the photometric properties of the images. As a consequence, polarimetric signals can be obtained with standard polarimetric modulation without any significant artifact. With the expected improvements in computer hardware and algorithms, we anticipate that on-site real-time correction of solar images will be possible in the near future.

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11

Litzenberg, Dale W., James M. Balter, Scott W. Hadley, Daniel A. Hamstra, Twyla R. Willoughby, Patrick A. Kupelian, Toufik Djemil et al. „Prostate Intrafraction Translation Margins for Real-Time Monitoring and Correction Strategies“. Prostate Cancer 2012 (2012): 1–6. http://dx.doi.org/10.1155/2012/130579.

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The purpose of this work is to determine appropriate radiation therapy beam margins to account for intrafraction prostate translations for use with real-time electromagnetic position monitoring and correction strategies. Motion was measured continuously in 35 patients over 1157 fractions at 5 institutions. This data was studied using van Herk's formula of (αΣ+γσ') for situations ranging from no electromagnetic guidance to automated real-time corrections. Without electromagnetic guidance, margins of over 10 mm are necessary to ensure 95% dosimetric coverage while automated electromagnetic guidance allows the margins necessary for intrafraction translations to be reduced to submillimeter levels. Factors such as prostate deformation and rotation, which are not included in this analysis, will become the dominant concerns as margins are reduced. Continuous electromagnetic monitoring and automated correction have the potential to reduce prostate margins to 2-3 mm, while ensuring that a higher percentage of patients (99% versus 90%) receive a greater percentage (99% versus 95%) of the prescription dose.

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12

Li, Jia Ying, Yun Chen Jiang und Lei Ren. „Real-Time Infrared Image Non-Uniformity Correction Based on FPGA“. Advanced Materials Research 971-973 (Juni 2014): 1696–99. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1696.

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IRFPA is the main direction of infrared imaging technology at present. It has high sensitivity and detection capability, but it also has disadvantages such as bad non-uniformity. Non-uniformity correction is a key technology in the application of IRFPA. As an applicable and real time non-uniformity correction method, the two-point correction algorithmic and single-point correction algorithmic are used widely. Their flow is simple and fixed. They are also suitable to be implemented by FPGA. In this paper, the two-point and single-point method of non-uniformity correction based on FPGA are introduced. And whether the two-point correction or the single-point correction is taken is determined by external control signal. After the completion of the correction coefficients calculation, the coefficients are written into FLASH so that the data will not be lost when the system is powered off.

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13

Xie, Kun, Wenguang Liu, Qiong Zhou, Zongfu Jiang, Fengjie Xi und Xiaojun Xu. „Real-time phase measurement and correction of dynamic multimode beam using a single spatial light modulator“. Chinese Optics Letters 18, Nr. 1 (2020): 011404. http://dx.doi.org/10.3788/col202018.011404.

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14

YANG, Ci-Yin, Jian-Ping ZHANG und Li-Hua CAO. „Infrared radiation measurement based on real-time correction“. JOURNAL OF INFRARED AND MILLIMETER WAVES 30, Nr. 3 (20.03.2012): 284–88. http://dx.doi.org/10.3724/sp.j.1010.2011.00284.

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15

Rukosuev, A. L., A. V. Kudryashov, A. N. Lylova, V. V. Samarkin und Yu V. Sheldakova. „Adaptive optics system for real-time wavefront correction“. Atmospheric and Oceanic Optics 28, Nr. 4 (Juli 2015): 381–86. http://dx.doi.org/10.1134/s1024856015040119.

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16

Lee, Christine C., Clifford R. Jack, Roger C. Grimm, Phillip J. Rossman, Joel P. Felmlee, Richard L. Ehman und Stephen J. Riederer. „Real-time adaptive motion correction in functional MRI“. Magnetic Resonance in Medicine 36, Nr. 3 (September 1996): 436–44. http://dx.doi.org/10.1002/mrm.1910360316.

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17

Benner, Thomas, André J. W. van der Kouwe, John E. Kirsch und A. Gregory Sorensen. „Real-time RF pulse adjustment forB0 drift correction“. Magnetic Resonance in Medicine 56, Nr. 1 (2006): 204–9. http://dx.doi.org/10.1002/mrm.20936.

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18

Li, Gansheng. „Correction to Making C++ Concurrent and Real-time“. ACM SIGPLAN Notices 33, Nr. 4 (April 1998): 27. http://dx.doi.org/10.1145/278283.607953.

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19

Bue, Brian D., David R. Thompson, Michael Eastwood, Robert O. Green, Bo-Cai Gao, Didier Keymeulen, Charles M. Sarture, Alan S. Mazer und Huy H. Luong. „Real-Time Atmospheric Correction of AVIRIS-NG Imagery“. IEEE Transactions on Geoscience and Remote Sensing 53, Nr. 12 (Dezember 2015): 6419–28. http://dx.doi.org/10.1109/tgrs.2015.2439215.

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20

Kandel, Mikhail E., Michael Fanous, Catherine Best-Popescu und Gabriel Popescu. „Real-time halo correction in phase contrast imaging“. Biomedical Optics Express 9, Nr. 2 (16.01.2018): 623. http://dx.doi.org/10.1364/boe.9.000623.

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21

Di Giuseppe, Francesca, Franco Molteni und Emanuel Dutra. „Real-time correction of ERA-Interim monthly rainfall“. Geophysical Research Letters 40, Nr. 14 (19.07.2013): 3750–55. http://dx.doi.org/10.1002/grl.50670.

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22

Chumchean, Siriluk, Ashish Sharma und Alan Seed. „An Integrated Approach to Error Correction for Real-Time Radar-Rainfall Estimation“. Journal of Atmospheric and Oceanic Technology 23, Nr. 1 (01.01.2006): 67–79. http://dx.doi.org/10.1175/jtech1832.1.

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Abstract A procedure for estimating radar rainfall in real time consists of three main steps: 1) the measurement of reflectivity and removal of known sources of errors, 2) the conversion of the reflectivity to a rainfall rate (Z–R conversion), and 3) the adjustment of the mean field bias as assessed using a rain gauge network. Error correction is associated with the first two steps and incorporates removing erroneous measurements and correcting biases in the Z–R conversion. This paper investigates the relative importance of error correction and the mean field bias–adjustment processes. In addition to the correction for ground clutter, the bright band, and hail, the two error correction strategies considered here are 1) a scale transformation function to remove range-dependent bias in measured reflectivity resulting from an increase in observation volume with range, and 2) the classification of storm types to account for the variation in Z–R relationships for convective and stratiform rainfall. The mean field bias is removed using two alternatives: 1) estimation of the bias at each time step based on the sample of observations available, and 2) use of a Kalman filter to estimate the bias under assumptions of a Markovian dependence structure. A 7-month record of radar and rain gauge rainfall for Sydney, Australia, were used in this study. The results show a stepwise decrease in the root-mean-square error (rmse) of radar rainfall with added levels of error correction using either of the two mean field bias–adjustment methods considered in our study. It was found that although the effects of the two error correction strategies were small compared to bias adjustment, they do form an important step of radar-rainfall estimation.

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23

Kelevitz, Krisztina, Kristy F. Tiampo und Brianna D. Corsa. „Improved Real-Time Natural Hazard Monitoring Using Automated DInSAR Time Series“. Remote Sensing 13, Nr. 5 (25.02.2021): 867. http://dx.doi.org/10.3390/rs13050867.

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As part of the collaborative GeoSciFramework project, we are establising a monitoring system for the Yellowstone volcanic area that integrates multiple geodetic and seismic data sets into an advanced cyber-infrastructure framework that will enable real-time streaming data analytics and machine learning and allow us to better characterize associated long- and short-term hazards. The goal is to continuously ingest both remote sensing (GNSS, DInSAR) and ground-based (seismic, thermal and gas observations, strainmeter, tiltmeter and gravity measurements) data and query and analyse them in near-real time. In this study, we focus on DInSAR data processing and the effects from using various atmospheric corrections and real-time orbits on the automated processing and results. We find that the atmospheric correction provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) is currently the most optimal for automated DInSAR processing and that the use of real-time orbits is sufficient for the early-warning application in question. We show analysis of atmospheric corrections and using real-time orbits in a test case over the Kilauea volcanic area in Hawaii. Finally, using these findings, we present results of displacement time series in the Yellowstone area between May 2018 and October 2019, which are in good agreement with GNSS data where available. These results will contribute to a baseline model that will be the basis of a future early-warning system that will be continuously updated with new DInSAR data acquisitions.

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Guo, Jiang, Jun Cheng, Yu Guo und Jian Xin Pang. „A Real-Time Dynamic Gesture Recognition System“. Applied Mechanics and Materials 333-335 (Juli 2013): 849–55. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.849.

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In this paper, we present a dynamic gesture recognition system. We focus on the visual sensory information to recognize human activity in form of hand movements from a small, predefined vocabulary. A fast and effective method is presented for hand detection and tracking at first for the trajectory extraction. A novel trajectory correction method is applied for simply but effectively trajectory correction. Gesture recognition is achieved by means of a matching technique by determining the distance between the unknown input direction code sequence and a set of previously defined templates. A dynamic time warping (DTW) algorithm is used to perform the time alignment and normalization by computing a temporal transformation allowing the two signals to be matched. Experiment results show our proposed gesture recognition system achieve well result in real time.

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25

Yu, Xiao Yu, und Yan Liu. „FBG Demodulation System Based on Real-Time Correction Technology“. Advanced Materials Research 505 (April 2012): 362–66. http://dx.doi.org/10.4028/www.scientific.net/amr.505.362.

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The system error of the FBG demodulation system based on tunable fiber Fabry-Perot (F-P) interferometer increases due to the uncertain relationship of driving voltage and the scanning wave peak. So the real-time correction technology is studied here. A parallel real-time correction system is designed to correct nonlinear problems of PZT and structural errors of the tunable FP interferometer. The relation between PZT driving voltage and the peak of tunable FP interferometer is analyzed by the experiments. Then a fast segmentation algorithm is designed. Then a signal processing system is designed using FPGA, which guarantees real-time work of the system by integrating the demodulation system control, filter calculation and data compression circuits into one FPGA chip. Experimental results show that the system described here solves the accuracy problem caused by the tunable FP interferometer.

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Roj, J. „Neural Network Based Real-time Correction of Transducer Dynamic Errors“. Measurement Science Review 13, Nr. 6 (01.12.2013): 286–91. http://dx.doi.org/10.2478/msr-2013-0042.

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Abstract In order to carry out real-time dynamic error correction of transducers described by a linear differential equation, a novel recurrent neural network was developed. The network structure is based on solving this equation with respect to the input quantity when using the state variables. It is shown that such a real-time correction can be carried out using simple linear perceptrons. Due to the use of a neural technique, knowledge of the dynamic parameters of the transducer is not necessary. Theoretical considerations are illustrated by the results of simulation studies performed for the modeled second order transducer. The most important properties of the neural dynamic error correction, when emphasizing the fundamental advantages and disadvantages, are discussed.

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27

Park, Young-Keun, Jae-Min Kim, Seong-Dong Lee und Hyun Ah Lee. „Context Based Real-time Korean Writing Correction for Foreigners“. Journal of KIISE 44, Nr. 10 (31.10.2017): 1087–93. http://dx.doi.org/10.5626/jok.2017.44.10.1087.

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28

Jereb, Zmago, und Janez Diaci. „Real-time geometrical correction of video image using FPGA“. IEICE Electronics Express 7, Nr. 5 (2010): 346–51. http://dx.doi.org/10.1587/elex.7.346.

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29

Mazurek, P. „Real–Time Correction of Cameras’ Geometric Distortions using GPGPU“. IFAC Proceedings Volumes 42, Nr. 13 (2009): 332–35. http://dx.doi.org/10.3182/20090819-3-pl-3002.00058.

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30

LI Geng-fei, 李赓飞, 李桂菊 LI Gui-ju, 韩广良 HAN Guang-liang, 刘培勋 LIU Pei-xun und 江山 JIANG Shan. „Real-time non-uniformity correction of infrared imaging system“. Optics and Precision Engineering 24, Nr. 11 (2016): 2841–47. http://dx.doi.org/10.3788/ope.20162411.2841.

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31

Cohen, M. S., R. A. Dubois und W. L. Scheding. „Rapid Artifact Detection and Correction for Real-Time fMRI“. NeuroImage 7, Nr. 4 (Mai 1998): S564. http://dx.doi.org/10.1016/s1053-8119(18)31397-1.

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32

Granholm, George R., Ronald J. Proulx, Paul J. Cefola, Andrey I. Nazarenko und Vasiliy S. Yurasov. „Requirements for Accurate Near-Real Time Atmospheric Density Correction“. Journal of the Astronautical Sciences 50, Nr. 1 (März 2002): 71–97. http://dx.doi.org/10.1007/bf03546331.

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33

Horenstein, M., T. Bifano, S. Pappas, J. Perreault und R. Krishnamoorthy-Mali. „Real time optical correction using electrostatically actuated MEMS devices“. Journal of Electrostatics 46, Nr. 2-3 (April 1999): 91–101. http://dx.doi.org/10.1016/s0304-3886(99)00015-7.

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34

Mayeli, Ahmad, Vadim Zotev, Hazem Refai und Jerzy Bodurka. „Real-time EEG artifact correction during fMRI using ICA“. Journal of Neuroscience Methods 274 (Dezember 2016): 27–37. http://dx.doi.org/10.1016/j.jneumeth.2016.09.012.

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35

Bax, Michael R., und Ramin Shahidi. „Real-time lens distortion correction: speed, accuracy and efficiency“. Optical Engineering 53, Nr. 11 (06.11.2014): 113103. http://dx.doi.org/10.1117/1.oe.53.11.113103.

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36

Zhu, Lin, Yiping Cao, Dawu He und Cheng Chen. „Grayscale imbalance correction in real-time phase measuring profilometry“. Optics Communications 376 (Oktober 2016): 72–80. http://dx.doi.org/10.1016/j.optcom.2016.05.013.

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37

Jing, Yun. „Real-time phase correction for transcranial focused ultrasound surgery“. Journal of the Acoustical Society of America 131, Nr. 4 (April 2012): 3210. http://dx.doi.org/10.1121/1.4707969.

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38

Xie, Quancai, Qiang Ma, Jingfa Zhang und Haiying Yu. „Study on real-time correction of site amplification factor“. Natural Hazards and Earth System Sciences 19, Nr. 12 (13.12.2019): 2827–39. http://dx.doi.org/10.5194/nhess-19-2827-2019.

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Abstract. The site amplification factor was usually considered to be scalar values, such as amplification of peak ground acceleration or peak ground velocity, or increments of seismic intensity in the earthquake early warning (EEW) system or seismic-intensity repaid report system. This paper focuses on evaluating an infinite impulse recursive filter method that could produce frequency-dependent site amplification and compare the performance of the scalar-value method with the infinite impulse recursive filter method. A large number of strong motion data of IBRH10 and IBRH19 of the Kiban Kyoshin network (KiK-net) triggered in more than 1000 earthquakes from 2004 to 2012 were selected carefully and used to obtain the relative site amplification ratio; we model the relative site amplification factor with a casual filter. Then we make a simulation from the borehole to the surface and also simulate from the front-detection station to the far-field station. Compared to different simulation cases, it can easily be found that this method could produce different amplification factors for different earthquakes and could reflect the frequency-dependent nature of site amplification. Through these simulations between two stations, we can find that the frequency-dependent correction for site amplification shows better performance than the amplification factor relative to velocity (ARV) method and station correction method. It also shows better performance than the average level and the highest level of the Japan Meteorological Agency (JMA) earthquake early warning system in ground motion prediction. Some cases in which simulation did not work very well were also found; possible reasons and problems were analyzed and addressed. This method pays attention to the amplitude and ignores the phase characteristics; this problem may be improved by the seismic-interferometry method. Frequency-dependent correction for site amplification in the time domain highly improves the accuracy of predicting ground motion in real time.

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Barral, Joëlle K., Juan M. Santos, Edward J. Damrose, Nancy J. Fischbein und Dwight G. Nishimura. „Real-time motion correction for high-resolution larynx imaging“. Magnetic Resonance in Medicine 66, Nr. 1 (24.02.2011): 174–79. http://dx.doi.org/10.1002/mrm.22773.

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40

Aksoy, Murat, Christoph Forman, Matus Straka, Stefan Skare, Samantha Holdsworth, Joachim Hornegger und Roland Bammer. „Real-time optical motion correction for diffusion tensor imaging“. Magnetic Resonance in Medicine 66, Nr. 2 (22.03.2011): 366–78. http://dx.doi.org/10.1002/mrm.22787.

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41

BANVILLE, SIMON, und RICHARD B. LANGLEY. „Instantaneous Cycle-Slip Correction for Real-Time PPP Applications“. Navigation 57, Nr. 4 (Dezember 2010): 325–34. http://dx.doi.org/10.1002/j.2161-4296.2010.tb01786.x.

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42

Liang, Ningxin, Guile Wu, Wenxiong Kang, Zhiyong Wang und David Dagan Feng. „Real-Time Long-Term Tracking With Prediction-Detection-Correction“. IEEE Transactions on Multimedia 20, Nr. 9 (September 2018): 2289–302. http://dx.doi.org/10.1109/tmm.2018.2803518.

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43

Innocente, P., D. Mazon, E. Joffrin und M. Riva. „Real-time fringe correction algorithm for the JET interferometer“. Review of Scientific Instruments 74, Nr. 8 (August 2003): 3645–52. http://dx.doi.org/10.1063/1.1593810.

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44

Wahab, Y., N. A. Bakar und M. Mazalan. „Error Correction for Foot Clearance in Real-Time Measurement“. Journal of Physics: Conference Series 495 (04.04.2014): 012046. http://dx.doi.org/10.1088/1742-6596/495/1/012046.

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45

Woldeselassie, Tilahun. „Precise real-time correction of Anger camera deadtime losses“. Medical Physics 29, Nr. 7 (24.06.2002): 1599–610. http://dx.doi.org/10.1118/1.1485996.

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46

Trahey, G. „An experimental system for real-time phase aberration correction“. Ultrasonic Imaging 11, Nr. 2 (April 1989): 128. http://dx.doi.org/10.1016/0161-7346(89)90019-9.

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47

Litzenberg, D. W., T. Willoughby, P. Kupelian, T. Djemil, A. Mahadevan, S. Jani, G. Weinstein, T. Solberg, C. Enke und L. Levine. „Prostate Margins for Real-Time Monitoring and Correction Strategies“. International Journal of Radiation Oncology*Biology*Physics 69, Nr. 3 (November 2007): S676—S677. http://dx.doi.org/10.1016/j.ijrobp.2007.07.2037.

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48

Barabashov, B. G., O. Maltseva und O. Pelevin. „Near real time IRI correction by TEC-GPS data“. Advances in Space Research 37, Nr. 5 (Januar 2006): 978–82. http://dx.doi.org/10.1016/j.asr.2006.02.008.

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49

Guo, Yudong, Juyong Zhang, Yihua Chen, Hongrui Cai, Zhangjin Huang und Bailin Deng. „Real-time face view correction for front-facing cameras“. Computational Visual Media 7, Nr. 4 (27.04.2021): 437–52. http://dx.doi.org/10.1007/s41095-021-0215-y.

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AbstractFace views are particularly important in person-to-person communication. Differenes between the camera location and the face orientation can result in undesirable facial appearances of the participants during video conferencing. This phenomenon is particularly noticeable when using devices where the front-facing camera is placed in unconventional locations such as below the display or within the keyboard. In this paper, we take a video stream from a single RGB camera as input, and generate a video stream that emulates the view from a virtual camera at a designated location. The most challenging issue in this problem is that the corrected view often needs out-of-plane head rotations. To address this challenge, we reconstruct the 3D face shape and re-render it into synthesized frames according to the virtual camera location. To output the corrected video stream with natural appearance in real time, we propose several novel techniques including accurate eyebrow reconstruction, high-quality blending between the corrected face image and background, and template-based 3D reconstruction of glasses. Our system works well for different lighting conditions and skin tones, and can handle users wearing glasses. Extensive experiments and user studies demonstrate that our method provides high-quality results.

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Li, Bing, Zheng Yu Yang und Bao Ma. „Research of IRFPA Non-uniformity Real-Time Correction Based on SOPC“. Key Engineering Materials 474-476 (April 2011): 277–82. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.277.

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<b>N</b>on-uniformity of infrared focal plane arrays (IRFPA) decreases the quality of the infrared imaging system greatly, so it is necessary to correct non-uniformity. Now the scene-based correction is being the focus of the study at home and abroad. Firstly, researching on normalized BP artificial neural network correction method in this paper, and then building a SOPC system on Altera's Stratix II EP2S60 DSP Development Board to realize the normalized BP real-time correction non-uniformity. The simulation results show that the SOPC system would meet the requirements of real-time correction. At the same time, the other method could be better to upgrade.

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