Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Farideh Foroozandeh Shahraki"'
Autor:
Rohith Reddy, Farideh Foroozandeh Shahraki, Chalapathi Gajjela, David Mayerich, Saurabh Prasad, Rupali Mankar
Publikováno v:
Analyst
Mid-infrared Spectroscopic Imaging (MIRSI) provides spatially-resolved molecular specificity by measuring wavelength-dependent mid-infrared absorbance. Infrared microscopes use large numerical aperture objectives to obtain high-resolution images of h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f47262f3272dae4b9a83d8a7b92b90f5
https://pubmed.ncbi.nlm.nih.gov/34198314
https://pubmed.ncbi.nlm.nih.gov/34198314
Publikováno v:
IGARSS
How to efficiently exploit useful information from hyperspectral data by joint analysis of spectral and spatial information is an important problem. In this work, we demonstrate a fusion network that can leverage recent developments in graph convolut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7605f3d1d2e0dad13b51e93dde2ead9a
https://doi.org/10.1109/igarss39084.2020.9323658
https://doi.org/10.1109/igarss39084.2020.9323658
Publikováno v:
Hyperspectral Image Analysis ISBN: 9783030386160
Deep neural networks have emerged as a set of robust machine learning tools for computer vision. The suitability of convolutional and recurrent neural networks, along with their variants, is well documented for color image analysis. However, remote s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::23b5558fbaf80b7563272f082d60ef13
https://doi.org/10.1007/978-3-030-38617-7_3
https://doi.org/10.1007/978-3-030-38617-7_3
Autor:
Farideh Foroozandeh Shahraki, Saurabh Prasad, Sebastian Berisha, Leila Saadatifard, Mahsa Lotfollahi, David Mayerich
Publikováno v:
Hyperspectral Image Analysis ISBN: 9783030386160
Deep neural networks are emerging as a popular choice for hyperspectral image analysis—compared with other machine learning approaches, they are more effective for a variety of applications in hyperspectral imaging. Part I (Chap. 3) introduces the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b719be4b630158c06de35bc46cc08d5
https://doi.org/10.1007/978-3-030-38617-7_4
https://doi.org/10.1007/978-3-030-38617-7_4
Autor:
Saurabh Prasad, Farideh Foroozandeh Shahraki, Mohamadkazem Safaripoorfatide, Nikolaos Karantzas, Demetrio Labate
Publikováno v:
Wavelets and Sparsity XVIII.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0a02f6dabb7fdbacf0641c82ed9edfed
https://doi.org/10.1117/12.2527712
https://doi.org/10.1117/12.2527712
Publikováno v:
GlobalSIP
Graph based manifold learning and embedding techniques have been very successful at representing high dimensional hyperspectral data in lower dimensions for visualization and classification. Graph based convolutional neural networks (GCNs) have been
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66462e8383bf9063f5adb2c39f082bf8
https://doi.org/10.1109/globalsip.2018.8645969
https://doi.org/10.1109/globalsip.2018.8645969
The reconstruction from sparse-view projections is one of important problems in computed tomography (CT) limited by the availability or feasibility of obtaining of a large number of projections. Traditionally, convex regularizers have been exploited
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f03822769adc1f7b2aee9cf27808b90
Publikováno v:
International Journal on Artificial Intelligence Tools. 26:1750015
Due to the growing number of cyclist accidents on urban roads, methods for collecting information on cyclists are of significant importance to the Department of Transportation. The collected information provides insights into solving critical problem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d43f22cf9874178cc5a95a0c8a3e3249
https://doi.org/10.1142/s0218213017500154
https://doi.org/10.1142/s0218213017500154