Relevant bibliographies by topics / Time of flight imaging

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Time of flight imaging'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Time of flight imaging"

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Heide, Felix, Wolfgang Heidrich, Matthias Hullin, and Gordon Wetzstein. "Doppler time-of-flight imaging." ACM Transactions on Graphics 34, no. 4 (July 27, 2015): 1–11. http://dx.doi.org/10.1145/2766953.

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Achar, Supreeth, Joseph R. Bartels, William L. 'Red' Whittaker, Kiriakos N. Kutulakos, and Srinivasa G. Narasimhan. "Epipolar time-of-flight imaging." ACM Transactions on Graphics 36, no. 4 (July 20, 2017): 1–8. http://dx.doi.org/10.1145/3072959.3073686.

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Hebden, Jeremy C., and Robert A. Kruger. "Transillumination imaging performance: A time-of-flight imaging system." Medical Physics 17, no. 3 (May 1990): 351–56. http://dx.doi.org/10.1118/1.596514.

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Giacomantone, Javier, María Lucía Violini, and Luciano Lorenti. "Background Subtraction for Time of Flight Imaging." Journal of Computer Science and Technology 17, no. 02 (October 1, 2017): e18. http://dx.doi.org/10.24215/16666038.17.e18.

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A time of flight camera provides two types of images simultaneously, depth and intensity. In this paper a computational method for background subtraction, combining both images or fast sequences of images, is proposed. The background model is based on unbalanced or semi-supervised classifiers, in particular support vector machines. A brief review of one class support vector machines is first given. A method that combines the range and intensity data in two operational modes is then provided. Finally, experimental results are presented and discussed.
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Surti, S. "Update on Time-of-Flight PET Imaging." Journal of Nuclear Medicine 56, no. 1 (December 18, 2014): 98–105. http://dx.doi.org/10.2967/jnumed.114.145029.

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Halimeh, Jad C., and Martin Wegener. "Time-of-flight imaging of invisibility cloaks." Optics Express 20, no. 1 (December 19, 2011): 63. http://dx.doi.org/10.1364/oe.20.000063.

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Hahne, Uwe, and Marc Alexa. "Exposure Fusion for Time-Of-Flight Imaging." Computer Graphics Forum 30, no. 7 (September 2011): 1887–94. http://dx.doi.org/10.1111/j.1467-8659.2011.02041.x.

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Kadambi, Achuta, Hang Zhao, Boxin Shi, and Ramesh Raskar. "Occluded Imaging with Time-of-Flight Sensors." ACM Transactions on Graphics 35, no. 2 (May 25, 2016): 1–12. http://dx.doi.org/10.1145/2836164.

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Lewellen, Tom K. "Time-of-flight PET." Seminars in Nuclear Medicine 28, no. 3 (July 1998): 268–75. http://dx.doi.org/10.1016/s0001-2998(98)80031-7.

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Anderson, Charles M., and Ralph E. Lee. "TIME-OF-FLIGHT TECHNIQUES." Magnetic Resonance Imaging Clinics of North America 1, no. 2 (December 1993): 217–27. http://dx.doi.org/10.1016/s1064-9689(21)00303-2.

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Dissertations / Theses on the topic "Time of flight imaging"

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Petcher, P. A. "Time of flight diffraction and imaging (TOFDI)." Thesis, University of Warwick, 2011. http://wrap.warwick.ac.uk/49478/.

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Time of flight diffraction and imaging (TOFDI) is based on time of flight diffraction (TOFD), adding cross-sectional imaging of the sample bulk by exploiting the scattering of ultrasonic waves from bulk defects in metals. Multiple wave modes are emitted by a pulsed laser ultrasound ablative source, and received by a sparse array of receiving electromagnetic acoustic transducers (EMATs), for non-contact (linear) scanning, with mode-conversions whenever waves are scattered. Standard signal processing techniques, such as band-pass filters, reduce noise. A B-scan is formed from multiple data captures (A-scans), with time and scan position axes, and colour representing amplitude or magnitude. B-scans may contain horizontal lines from surface waves propagating directly from emitter to receiver, or via a back-wall, and angled lines after reflection off a surface edge. A Hough transform (HT), modified to deal with the constraints of a B-scan, can remove such lines. A parabola matched filter has been developed that identifies the features in the B-scan caused by scattering from point-like defects, reducing them to peaks and minimising noise. Multiple B-scans are combined to reduce noise further. The B-scan is also processed to form a cross-sectional image, enabling detection and positioning of multiple defects. The standard phase correlation technique applied to camera images, has been used to track the relative position between transducer and sample. Movement has been determined to sub-pixel precision, with a median accuracy of 0.01mm of linear movement (0.06 of a pixel), despite uneven illumination and the use of a basic low resolution camera. The prototype application is testing rough steel products formed by continuous casting, but the techniques created to facilitate operation of TOFDI are applicable elsewhere.
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Mei, Jonathan (Jonathan B. ). "Algorithms for 3D time-of-flight imaging." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85609.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 57-58).
This thesis describes the design and implementation of two novel frameworks and processing schemes for 3D imaging based on time-of- flight (TOF) principles. The first is a low power, low hardware complexity technique based on parametric signal processing for orienting and localizing simple planar scenes. The second is an improved method for simultaneously performing phase unwrapping and denoising for sinusoidal amplitude modulated continuous wave ToF cameras using multiple frequencies. The first application uses several unfocused photodetectors with high time resolution to estimate information about features in the scene. Because the time profiles of the responses for each sensor are parametric in nature, the recovery algorithm uses finite rate of innovation (FRI) methods to estimate signal parameters. The signal parameters are then used to recover the scene features. The second application uses a generalized approximate message passing (GAMP) framework to incorporate both accurate probabilistic modeling for the measurement process and underlying scene depth map sparsity to accurately extend the unambiguous depth range of the camera. This joint processing results in improved performance over separate unwrapping and denoising steps.
by Jonathan Mei.
M. Eng.
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Lee, Jason W. L. "Novel developments in time-of-flight particle imaging." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:195be057-7ce0-4a15-b639-b08892fde312.

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In the field of physical chemistry, the relatively recently developed technique of velocity-map imaging has allowed chemical dynamics to be explored with a greater depth than could be previously achieved using other methods. Capturing the scattering image associated with the products resulting from fragmentation of a molecule allows the dissociative pathways and energy landscape to be investigated. In the study of particle physics, the neutron has become an irreplaceable spectroscopic tool due to the unique nature of the interaction with certain materials. Neutron spectroscopy is a non-destructive imaging technique that allows a number of properties to be discerned, including chemical identification, strain tensor measurements and the identification of beneath the sample surface using radiography and tomography. In both of these areas, as well as a multitude of other disciplines, a flight tube is used to separate particles, distinguishing them based upon their mass in the former case and their energy in the latter. The experiments can be vastly enhanced by the ability to record both the position and arrival time of the particle of interest. This thesis describes several new developments made in instrumentation for experiments involving time-of-flight particle imaging. The first development described is the construction of a new velocity-map imaging instrument that utilises electron ionisation to perform both steps of molecular fragmentation and ionisation. Data from CO2 is presented as an example of the ability of the instrument, and a preliminary analysis of the images is performed. The second presented project is the design of a time-resolved and position-resolved detector developed for ion imaging experiments. The hardware, software and firmware are described and presented alongside data from a variety of the experiments showcasing the breadth of investigations that are possible using the sensor. Finally, the modifications made to the detector to allow time-resolved neutron imaging are detailed, with an in-depth description of the various proof-of-concept experiments carried out as part of the development process.
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Calvert, N. "Time-of-flight Compton scatter imaging for cargo security." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1503664/.

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By measuring the time of flight of scattered x-ray photons, the point of interaction, assuming a single scatter, can be determined, providing a three dimensional image of cargo containers. The present work introduces the technique, and provides experimental and theoretical results to show the feasibility of such a technique. Monte Carlo simulations were performed to investigate the proportion of multiple scatter detected using a proposed experimental setup, and it was found that it accounted for almost 50% of the recorded signal. Monte Carlo simulations of a scintillation detector were provided and used to design the detectors used. Experimental measurements at a picosecond length x-ray source resulted in the reconstruction of scatter position of photons interacting in a 5 cm thick test object to an accuracy of 12 cm full width at half maximum. Preliminary experiments were also performed using a conventional commercially available linear accelerator with a pulse length of 170 ns. Deconvolution techniques were applied to the recorded data to estimate the position of scatter. An analytic single scatter forward model was derived from the radiative transfer equation, discretised and the inverse problem was solved using a number of different methods. The alternating direction method of multipliers was adapted for nonlinear problems and provided image quality that was comparable to the gold standard linear method.
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Yoon, Oh Kyu. "Continuous time-of-flight mass spectrometric imaging of fragmented ions /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Noraky, James. "Algorithms and systems for low power time-of-flight imaging." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127029.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 151-158).
Depth sensing is useful for many emerging applications that range from augmented reality to robotic navigation. Time-of-flight (ToF) cameras are appealing depth sensors because they obtain dense depth maps with minimal latency. However, for mobile and embedded devices, ToF cameras, which obtain depth by emitting light and estimating its roundtrip time, can be power-hungry and limit the battery life of the underlying device. To reduce the power for depth sensing, we present algorithms to address two scenarios. For applications where RGB images are concurrently collected, we present algorithms that reduce the usage of the ToF camera and estimate new depth maps without illuminating the scene. We exploit the fact that many applications operate in nearly rigid environments, and our algorithms use the sparse correspondences across the consecutive RGB images to estimate the rigid motion and use it to obtain new depth maps.
Our techniques can reduce the usage of the ToF camera by up to 85%, while still estimating new depth maps within 1% of the ground truth for rigid scenes and 1.74% for dynamic ones. When only the data from a ToF camera is used, we propose algorithms that reduce the overall amount of light that the ToF camera emits to obtain accurate depth maps. Our techniques use the rigid motions in the scene, which can be estimated using the infrared images that a ToF camera obtains, to temporally mitigate the impact of noise. We show that our approaches can reduce the amount of emitted light by up to 81% and the mean relative error of the depth maps by up to 64%. Our algorithms are all computationally efficient and can obtain dense depth maps at up to real-time on standard and embedded computing platforms.
Compared to applications that just use the ToF camera and incur the cost of higher sensor power and to those that estimate depth entirely using RGB images, which are inaccurate and have high latency, our algorithms enable energy-efficient, accurate, and low latency depth sensing for many emerging applications.
by James Noraky.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Westberg, Michael. "Time of Flight Based Teat Detection." Thesis, Linköping University, Department of Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-19292.

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Time of flight is an imaging technique with uses depth information to capture 3D information in a scene. Recent developments in the technology have made ToF cameras more widely available and practical to work with. The cameras now enable real time 3D imaging and positioning in a compact unit, making the technology suitable for variety of object recognition tasks

An object recognition system for locating teats is at the center of the DeLaval VMS, which is a fully automated system for milking cows. By implementing ToF technology as part of the visual detection procedure, it would be possible to locate and track all four teat’s positions in real time and potentially provide an improvement compared with the current system.

The developed algorithm for teat detection is able to locate teat shaped objects in scenes and extract information of their position, width and orientation. These parameters are determined with an accuracy of millimeters. The algorithm also shows promising results when tested on real cows. Although detecting many false positives the algorithm was able to correctly detected 171 out of 232 visible teats in a test set of real cow images. This result is a satisfying proof of concept and shows the potential of ToF technology in the field of automated milking.

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Bhandari, Ayush. "Inverse problems in time-of-flight imaging : theory, algorithms and applications." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/95867.

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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 100-108).
Time-of-Fight (ToF) cameras utilize a combination of phase and amplitude information to return real-time, three dimensional information of a scene in form of depth images. Such cameras have a number of scientific and consumer oriented applications. In this work, we formalize a mathematical framework that leads to unifying perspective on tackling inverse problems that arise in the ToF imaging context. Starting from first principles, we discuss the implications of time and frequency domain sensing of a scene. From a linear systems perspective, this amounts to an operator sampling problem where the operator depends on the physical parameters of a scene or the bio-sample being investigated. Having presented some examples of inverse problems, we discuss detailed solutions that benefit from scene based priors such sparsity and rank constraints. Our theory is corroborated by experiments performed using ToF/Kinect cameras. Applications of this work include multi-bounce light decomposition, ultrafast imaging and fluorophore lifetime estimation.
by Ayush Bhandari.
S.M.
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Winter, Benjamin. "Novel methods in imaging mass spectrometry and ion time-of-flight detection." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:43db5039-0490-4f97-8519-4d3ed4e30ca3.

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Imaging mass spectrometry (IMS) in microscope mode allows the spatially resolved molecular constitution of a large sample section to be analysed in a single experiment. If performed in a linear mass spectrometer, the applicability of microscope IMS is limited by a number of factors: the low mass resolving power of the employed ion optics; the time resolution afforded by the scintillator screen based particle detector and the multi-hit capability, per pixel, of the employed imaging sensor. To overcome these limitations, this thesis concerns the construction of an advanced ion optic employing a pulsed extraction method to gain a higher ToF resolution, the development of a bright scintillator screen with short emission lifetime, and the application of the Pixel Imaging Mass Spectrometry (PImMS) sensor with multi-mass imaging and time stamping capabilities. Initial experimental results employing a three electrode ion optic to spatially map ions emitted from a sample surface are presented. By applying a static electric potential a time-of-flight resolution of t/2Δt=54 and a spatial resolution of 20 μm are determined across a field-of-view of 4 mm diameter. While the moderate time-of-flight resolution only allows particles separated by a few Dalton to be distinguished, the instrument is used to demonstrate the multi-mass imaging capabilities of the PImMS sensor when being applied to image grid structures or tissue samples. An improved time-of-flight resolution is achieved by post extraction differential acceleration of a selected range of ions (up to 100 Da) using a newly developed five electrode ion optic. This modification is shown to correct the initial velocity spread of the ions coming off the sample surface, which yields an enhanced time-of-flight resolution of t/2Δt=2000 . The spatial resolution of the instrument is found to be 20 μm across a field-of-view of 4 mm. Adjusting the extraction field strength applied to the ion optic of the constructed mass spectrometer allows the optimised mass range to be tuned to any mass of interest. Ion images are recorded for various samples with comparable spatial and ToF resolution. Hence, studies on tissue sections and multi sample arrays become accessible with the improved design and operational principle of the microscope mode IMS instrument. A fast and efficient conversion of impinging ions into detectable flashes of light, which can consequently be recorded by a fast imaging sensor, is essential to maintain the achievable time-of-flight and spatial resolution of the IMS instrument constructed. In order to find a suitable fast and bright scintillator to be applied in a microchannel based particle detector, various inorganic and organic substances are characterised in terms of their emission properties following electron excitation. Poly-para-phenylene laser dye screens are found to show an outstanding performance among all substances analysed. An emission life time of below 4 ns and a brightness exceeding that of a P47 screen (industry standard) by a factor 2× is determined. No signal degradation is observed over an extended period, and the spatial resolution is found to be comparable to commercial imaging detectors. Hence, these scintillator screens are fully compatible with any ion imaging application requiring a high time resolution. In a further series of mass spectrometric experiments, ions are accelerated onto a scintillator mounted in front of a multi pixel photon counter. The charged particle impact stimulated the emission of a few photons, which are collected by the fast photon counter. Poly-para-phenylene laser dyes again show an outstanding efficiency for the conversion of ions into photons, resulting in a signal enhancement of up to 5× in comparison to previous experiments, which employed an inorganic LYSO scintillator.
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Mutamba, Q. B. "Time of flight imaging with 3MeV neutrons based on the associated particle technique." Thesis, Swansea University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.638284.

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Spatial imaging using the time of flight (TOF) method with 3 MeV neutrons based on the associated particle technique has been demonstrated to produce a tomographic image of an aluminium test object. The imaging set-up used coincidence detection of the neutron-induced inelastic scattered gamma rays from the 27Al(n,n'γ)27 Al nuclear reaction in the test object, and the associated 3He particles from the TiD self-implanted target. The test object was positioned 120 cm away from the target, and rotated in the neutron beam at 72 evenly spaced angles of orientation. The TOF spectrum data that were obtained at each angle were used in the image reconstruction computer programme that used the technique of filtered back-projection to produce the image of the test object. In order to remove deficiencies in the image reconstruction programme, it was tested first with computer simulated TOF spectrum data for the neutrons and the gamma rays that were gathered, using assumptions that were based on experimental conditions. At optimum timing resolution of 1.4 ns and a timing efficiency of 2.2% for the 3He-gamma coincidence were achieved. An equivalent dose of 69.1 μSv per scan was delivered to the test object, and it received a total dose of 4.98 mSv during the 72 scans. A good image of the test object was obtained that had clearly distinguishable walls above the background due to chance coincidence signals. With the possible improvements in the detection efficiency for gamma rays, the timing resolution, and the timing efficiency, we envisage a medical application of the imaging technique to provide spatial information of overloaded elements in parts of the human body as a result of certain diseases. We consider the imaging of aluminium in the bone for patients with chronic renal failure and that of iron in the liver for patients with a haemochromatosis.
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Books on the topic "Time of flight imaging"

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Grzegorzek, Marcin, Christian Theobalt, Reinhard Koch, and Andreas Kolb, eds. Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-44964-2.

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Mutto, Carlo Dal. Time-of-Flight Cameras and Microsoft Kinect™. Boston, MA: Springer US, 2012.

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Allāh, Imilī Naṣr. Flight against time. Charlottetown, P.E.I: Ragweed Press, 1987.

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Allāh, Imilī Naṣr. Flight against time. Austin, Tex: Center for Middle Eastern Studies, University of Texas at Austin, 1997.

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Allāh, Imilī Naṣr. Flight against time. Charlottetown, P.E.I: Ragweed Press, 1987.

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Kight, Pat. The flight of time. Corvallis, Or: printed by Cascade Printing, 1988.

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Hansard, Miles, Seungkyu Lee, Ouk Choi, and Radu Horaud. Time-of-Flight Cameras. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4658-2.

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Smith, Robert W. (Robert William), 1952-, ed. Hubble: Imaging space and time. Washington, D.C: National Geographic, 2011.

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1952-, Smith Robert W., ed. Hubble imaging space and time. Washington, D. C: National Geographic Society, 2008.

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Dougherty, Edward R. Introduction to real-time imaging. Bellingham, Wash., USA: SPIE Optical Engineering Press, 1995.

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Book chapters on the topic "Time of flight imaging"

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Broadstone, Steven R., and R. Martin Arthur. "Time-of-Flight Approximation for Medical Ultrasonic Imaging." In Acoustical Imaging, 165–74. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-0725-9_16.

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Sabatini, Angelo M. "Modeling in-Air Ultrasonic Time-of-Flight Noise." In Acoustical Imaging, 633–38. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4419-8772-3_103.

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Nieuwenhove, Daniël Van. "Time-of-Flight 3D-Imaging Techniques." In Interactive Displays, 233–49. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118706237.ch7.

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Böhme, Martin, Martin Haker, Kolja Riemer, Thomas Martinetz, and Erhardt Barth. "Face Detection Using a Time-of-Flight Camera." In Dynamic 3D Imaging, 167–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03778-8_13.

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Benaron, David A., David C. Ho, Stanley Spilman, John P. Van Houten, and David K. Stevenson. "Tomographic Time-of-Flight Optical Imaging Device." In Advances in Experimental Medicine and Biology, 207–14. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-1875-4_26.

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Lindner, Marvin, and Andreas Kolb. "Compensation of Motion Artifacts for Time-of-Flight Cameras." In Dynamic 3D Imaging, 16–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03778-8_2.

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Bleiweiss, Amit, and Michael Werman. "Fusing Time-of-Flight Depth and Color for Real-Time Segmentation and Tracking." In Dynamic 3D Imaging, 58–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03778-8_5.

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Kohoutek, Tobias K., David Droeschel, Rainer Mautz, and Sven Behnke. "Indoor Positioning and Navigation Using Time-Of-Flight Cameras." In TOF Range-Imaging Cameras, 165–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-27523-4_8.

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Fundamenski, W. R., M. P. Dolbey, and M. D. C. Moles. "Imaging of Defects in Thin-Walled Tubing Using Ultrasonic Time-of-Flight." In Acoustical Imaging, 581–87. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3370-2_91.

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Allemand, R. "Time-of-Flight Positron Emission Tomography (T.O.F. P.E.T.)." In Physics and Engineering of Medical Imaging, 902–12. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3537-2_72.

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Conference papers on the topic "Time of flight imaging"

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Antholzer, Stephan, Christoph Wolf, Michael Sandbichler, Markus Dielacher, and Markus Haltmeier. "Compressive time-of-flight imaging." In 2017 International Conference on Sampling Theory and Applications (SampTA). IEEE, 2017. http://dx.doi.org/10.1109/sampta.2017.8024403.

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Heide, Felix, Gordon Wetzstein, Matthias Hullin, and Wolfgang Heidrich. "Doppler time-of-flight imaging." In SIGGRAPH '15: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2782782.2792497.

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Li, Fengqiang, Huaijin Chen, Chia-Kai Yeh, Adithya Pediredla, Kuan He, Ashok Veeraghvan, and Oliver Cossairt. "Compressive Time-of-Flight Imaging." In Applied Industrial Optics: Spectroscopy, Imaging and Metrology. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/aio.2018.am2a.5.

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Li, Fengqiang, Huaijin Chen, Chiakai Yeh, Ashok Veeraraghavan, and Oliver Cossairt. "High spatial resolution time-of-flight imaging." In Computational Imaging III, edited by Amit Ashok, Jonathan C. Petruccelli, Abhijit Mahalanobis, and Lei Tian. SPIE, 2018. http://dx.doi.org/10.1117/12.2303794.

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Schaller, Christian, Andre Adelt, Jochen Penne, and Joachim Hornegger. "Time-of-Flight sensor for patient positioning." In SPIE Medical Imaging, edited by Michael I. Miga and Kenneth H. Wong. SPIE, 2009. http://dx.doi.org/10.1117/12.812498.

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Li, Fengqiang, Florian Willomitzer, Prasanna Rangarajan, Andreas Velten, Mohit Gupta, and Oliver Cossairt. "Micro Resolution Time-of-Flight Imaging." In 3D Image Acquisition and Display: Technology, Perception and Applications. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/3d.2018.3w2g.2.

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Li, Fengqiang, Florian Willomitzer, Prasanna Rangarajan, Andreas Velten, Mohit Gupta, and Oliver Cossairt. "Micro Resolution Time-of-Flight Imaging." In Computational Optical Sensing and Imaging. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/cosi.2018.cm2e.4.

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Charbon, Edoardo. "Introduction to time-of-flight imaging." In 2014 IEEE Sensors. IEEE, 2014. http://dx.doi.org/10.1109/icsens.2014.6985072.

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Velten, Andreas, Moungi Bawendi, and Ramesh Raskar. "Picosecond Camera for Time-of-Flight Imaging." In Imaging Systems and Applications. Washington, D.C.: OSA, 2011. http://dx.doi.org/10.1364/isa.2011.imb4.

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Ruiter, N. V., E. Kretzek, M. Zapf, T. Hopp, and H. Gemmeke. "Time of flight interpolated synthetic aperture focusing technique." In SPIE Medical Imaging, edited by Neb Duric and Brecht Heyde. SPIE, 2017. http://dx.doi.org/10.1117/12.2254259.

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Reports on the topic "Time of flight imaging"

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H. FUNSTEN. IMAGING TIME-OF-FLIGHT ION MASS SPECTROGRAPH. Office of Scientific and Technical Information (OSTI), November 2000. http://dx.doi.org/10.2172/768176.

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Copley, John R. D. Neutron time-of-flight spectroscopy. Gaithersburg, MD: National Institute of Standards and Technology, 1998. http://dx.doi.org/10.6028/nist.ir.6205.

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Marleau, Peter, Erik Brubaker, Mark D. Gerling, Patricia Frances Schuster, and John T. Steele. Time Encoded Radiation Imaging. Office of Scientific and Technical Information (OSTI), September 2011. http://dx.doi.org/10.2172/1113859.

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Rockwell, Donald. Space-Time Imaging Systems. Fort Belvoir, VA: Defense Technical Information Center, February 2009. http://dx.doi.org/10.21236/ada584973.

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Dietrick, Robert A. Hypersonic Flight: Time To Go Operational. Fort Belvoir, VA: Defense Technical Information Center, February 2013. http://dx.doi.org/10.21236/ad1018856.

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Zare, Richard N., Matthew D. Robbins, Griffin K. Barbula, and Richard Perry. Hadamard Transform Time-of-Flight Spectroscopy. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada564594.

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Chiang, I.-Hung, Adam Rusek, and M. Sivertz. Time of Flight of NSRL Beams. Office of Scientific and Technical Information (OSTI), October 2005. http://dx.doi.org/10.2172/1775544.

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Watson, Thomas B. Proton Transfer Time-of-Flight Mass Spectrometer. Office of Scientific and Technical Information (OSTI), March 2016. http://dx.doi.org/10.2172/1251396.

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Kponou, A., A. Hershcovitch, D. McCafferty, and F. Usack. A TIME-OF-FLIGHT SPECTROMETER FOR SuperEBIS. Office of Scientific and Technical Information (OSTI), January 1994. http://dx.doi.org/10.2172/1151297.

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Zare, Richard N., Matthew D. Robbins, Griffin K. Barbula, and Richard Perry. Hadamard Transform Time-of-Flight Mass Spectrometry. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada589689.

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