Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Neda Rohani"'
Publikováno v:
Sensors, Vol 21, Iss 18, p 6150 (2021)
Hyperspectral reflectance imaging in the short-wave infrared range (SWIR, “extended NIR”, ca. 1000 to 2500 nm) has proven to provide enhanced characterization of paint materials. However, the interpretation of the results remains challenging due
Externí odkaz:
https://doaj.org/article/fdfc828c71984c3aa231e4374be7f5fa
Autor:
Alice Lucas, Tucker Tomlinson, Neda Rohani, Raeed Chowdhury, Sara A. Solla, Aggelos K. Katsaggelos, Lee E. Miller
Publikováno v:
Frontiers in Systems Neuroscience, Vol 13 (2019)
Somatosensation is composed of two distinct modalities: touch, arising from sensors in the skin, and proprioception, resulting primarily from sensors in the muscles, combined with these same cutaneous sensors. In contrast to the wealth of information
Externí odkaz:
https://doaj.org/article/0bf099b17e0e4894a88cc7482a9af0de
Autor:
Kevin Crowston, Tae Kyoung Lee, Carsten Østerlund, Laura Trouille, Shane L. Larson, Corey Brian Jackson, Scott Coughlin, J. R. Smith, Mahboobeh Harandi, Sara Bahaadini, Sarah Allen, Michael Zevin, Aggelos K. Katsaggelos, Neda Rohani
Publikováno v:
IEEE Transactions on Learning Technologies. 13:123-134
We present the design of a citizen science system that uses machine learning to guide the presentation of image classification tasks to newcomers to help them more quickly learn how to do the task while still contributing to the work of the project.
Publikováno v:
Pattern Recognition Letters. 116:80-87
This paper proposes a new model for multi-sensory data classification. To tackle this problem, probabilistic modeling and variational Bayesian inference are used. A Gaussian Process (GP) classifier is built upon the introduced modeling. Its posterior
Publikováno v:
Angewandte Chemie International Edition. 57:10910-10914
Nonlinear unmixing of hyperspectral reflectance data is one of the key problems in quantitative imaging of painted works of art. The approach presented is to interrogate a hyperspectral image cube by first decomposing it into a set of reflectance cur
Autor:
Scott Coughlin, Vicky Kalogera, Neda Rohani, Aggelos K. Katsaggelos, Vahid Noroozi, Sara Bahaadini, J. R. Smith, Michael Zevin
Publikováno v:
Information Sciences. 444:172-186
The detection of gravitational waves with ground-based laser-interferometric detectors requires sensitivity to changes in distance much smaller than the diameter of atomic nuclei. Though sophisticated machinery and techniques have been developed over
Publikováno v:
Pure and Applied Chemistry. 90:493-506
Visible hyperspectral imaging (HSI) is a fast and non-invasive imaging method that has been adapted by the field of conservation science to study painted surfaces. By collecting reflectance spectra from a 2D surface, the resulting 3D hyperspectral da
Publikováno v:
ICASSP
We argue that learning a hierarchy of features in a hierarchical dataset requires lower layers to approach convergence faster than layers above them. We show that, if this assumption holds, we can analytically approximate the outcome of stochastic gr
Publikováno v:
ICASSP
In this paper, the problem of automatic nonlinear unmixing of hyperspectral reflectance data using works of art as test cases is described. We use a deep neural network to decompose a given spectrum quantitatively to the abundance values of pure pigm
Autor:
Sara Bahaadini, Corey Brian Jackson, J. R. Smith, M. Zevin, Pablo Ruiz, Neda Rohani, S. B. Coughlin, Christopher P. L. Berry, Carsten Østerlund, J. S. Areeda, Laura Trouille, Kevin Crowston, Vahid Noroozi, Sarah Allen, O. Patane, Vicky Kalogera, Aggelos K. Katsaggelos, Andrew Lundgren, Michael W. Coughlin, Mahboobeh Harandi
Publikováno v:
Lundgren, A, Coughlin, S B, Bahaadini, S, Rohani, N, Zevin, M, Patane, O, Harandi, M, Jackson, C, Noroozi, Z, Allen, S, Areeda, J S, Coughlin, M, Ruiz, P, Berry, C P L, Crowston, K, Katsaggelos, A K, Osterlund, C, Smith, J R, Trouille, L & Kalogera, V 2019, ' Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning ', Physical Review D, vol. 99, no. 8, 082002 . https://doi.org/10.1103/PhysRevD.99.082002
The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitation