Zobrazeno 1 - 10
of 216
pro vyhledávání: '"Schechner Yoav Y."'
Significant uncertainty in climate prediction and cloud physics is tied to observational gaps relating to shallow scattered clouds. Addressing these challenges requires remote sensing of their three-dimensional (3D) heterogeneous volumetric scatterin
Externí odkaz:
http://arxiv.org/abs/2403.05932
Autor:
Heese Birgit, Hofer Julian, Baars Holger, Engelmann Ronny, Althausen Dietrich, Schechner Yoav Y.
Publikováno v:
EPJ Web of Conferences, Vol 176, p 05049 (2018)
Optical properties of fresh biomass burning aerosol were measured by lidar during the wild fires in Israel in November 2016. A single-wavelength lidar Polly was operated at the Technion Campus at Haifa. The detector with originally two channels at 53
Externí odkaz:
https://doaj.org/article/a901091a736547708f73a51934af11b9
In diverse microscopy modalities, sensors measure only real-valued intensities. Additionally, the sensor readouts are affected by Poissonian-distributed photon noise. Traditional restoration algorithms typically aim to minimize the mean squared error
Externí odkaz:
http://arxiv.org/abs/2212.03235
Autor:
Czerninski, Ido, Schechner, Yoav Y.
Inverse rendering seeks to estimate scene characteristics from a set of data images. The dominant approach is based on differential rendering using Monte-Carlo. Algorithms as such usually rely on a forward model and use an iterative gradient method t
Externí odkaz:
http://arxiv.org/abs/2110.00085
Autor:
Sharma, Prafull, Aittala, Miika, Schechner, Yoav Y., Torralba, Antonio, Wornell, Gregory W., Freeman, William T., Durand, Fredo
We present a passive non-line-of-sight method that infers the number of people or activity of a person from the observation of a blank wall in an unknown room. Our technique analyzes complex imperceptible changes in indirect illumination in a video o
Externí odkaz:
http://arxiv.org/abs/2108.13027
We introduce new adjustments and advances in space-borne 3D volumetric scattering-tomography of cloud micro-physics. The micro-physical properties retrieved are the liquid water content and effective radius within a cloud. New adjustments include an
Externí odkaz:
http://arxiv.org/abs/2103.10305
We present 3DeepCT, a deep neural network for computed tomography, which performs 3D reconstruction of scattering volumes from multi-view images. Our architecture is dictated by the stationary nature of atmospheric cloud fields. The task of volumetri
Externí odkaz:
http://arxiv.org/abs/2012.05960
We derive computed tomography (CT) of a time-varying volumetric translucent object, using a small number of moving cameras. We particularly focus on passive scattering tomography, which is a non-linear problem. We demonstrate the approach on dynamic
Externí odkaz:
http://arxiv.org/abs/2012.03223
Tomography aims to recover a three-dimensional (3D) density map of a medium or an object. In medical imaging, it is extensively used for diagnostics via X-ray computed tomography (CT). Optical diffusion tomography is an alternative to X-ray CT that u
Externí odkaz:
http://arxiv.org/abs/2005.11423
Autor:
Golts, Alex, Schechner, Yoav Y.
Computer vision tasks are often expected to be executed on compressed images. Classical image compression standards like JPEG 2000 are widely used. However, they do not account for the specific end-task at hand. Motivated by works on recurrent neural
Externí odkaz:
http://arxiv.org/abs/2003.12618