Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Matthew D. Reisman"'
Autor:
Joseph P. Culver, Joseph D. Dougherty, Lindsey M. Brier, Annie R. Bice, Rachel M. Rahn, Matthew D. Reisman
Publikováno v:
Cereb Cortex
Cross-sectional studies have established a variety of structural, synaptic, and cell physiological changes corresponding to critical periods in cortical development. However, the emergence of functional connectivity (FC) in development has not been f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::193c630568a312832fae6d3d3fe56a21
https://doi.org/10.1093/cercor/bhab312
https://doi.org/10.1093/cercor/bhab312
Autor:
Patrick W Wright, Lindsey M Brier, Adam Q Bauer, Grant A Baxter, Andrew W Kraft, Matthew D Reisman, Annie R Bice, Abraham Z Snyder, Jin-Moo Lee, Joseph P Culver
Publikováno v:
PLoS ONE, Vol 12, Iss 10, p e0185759 (2017)
The interplay between hemodynamic-based markers of cortical activity (e.g. fMRI and optical intrinsic signal imaging), which are an indirect and relatively slow report of neural activity, and underlying synaptic electrical and metabolic activity thro
Externí odkaz:
https://doaj.org/article/dbb20ca61ad74ab69ed44a3c8e6f3a27
Publikováno v:
Automatic Target Recognition XXXII.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d92b7f99786981b68460733bf5b864f
https://doi.org/10.1117/12.2621951
https://doi.org/10.1117/12.2621951
Publikováno v:
Geospatial Informatics XII.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5628a654e2b7e78c086460a294bbd81f
https://doi.org/10.1117/12.2621960
https://doi.org/10.1117/12.2621960
Publikováno v:
Automatic Target Recognition XXXI.
Deep learning-based classification of objects in overhead imagery is a difficult problem to solve due to low to moderate available resolution as well as wide ranges of scale between objects. Traditional machine learning object classification techniqu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b597393a3f1abc996158fdad46aa95ea
https://doi.org/10.1117/12.2587102
https://doi.org/10.1117/12.2587102
Publikováno v:
Automatic Target Recognition XXXI.
Many problems in defense and automatic target recognition (ATR) require concurrent detection and classification of objects of interest in wide field-of-view overhead imagery. Traditional machine learning approaches are optimized to perform either det
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6520c298b564f3dee7a4436866c36ef4
https://doi.org/10.1117/12.2586991
https://doi.org/10.1117/12.2586991
Publikováno v:
Automatic Target Recognition XXXI.
Change detection between two temporal scenes of overhead imagery is a common problem in many applications of computer vision and image processing. Traditional change detection techniques only provide a pixel level detail of change and are sensitive t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bffc8faf834ab88345042b44fd75f99f
https://doi.org/10.1117/12.2587080
https://doi.org/10.1117/12.2587080
Publikováno v:
AIPR
Deep-learning-based broad area damage/change assessment (BADA) monitors environmental change due to disaster damage. Our approach was developed while competing in the xView2 challenge. The purpose of the xView2 challenge was to provide a capability t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::711d6a465c948685de836f333f87eabd
https://doi.org/10.1109/aipr50011.2020.9425272
https://doi.org/10.1109/aipr50011.2020.9425272
Autor:
Patrick Owendro, T. L. Overman, Matthew D. Reisman, Larry D. Dewell, Eon Mattis, Jordan Rabold
Publikováno v:
Automatic Target Recognition XXX.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9c9ac9133bfa2313378d9b268bf8bd88
https://doi.org/10.1117/12.2558779
https://doi.org/10.1117/12.2558779
Autor:
Abraham Z. Snyder, Grant A. Baxter, Matthew D. Reisman, Jasmine J. Park, Annie R. Bice, Patrick W. Wright, Joseph P. Culver, Adam Q. Bauer, Jin-Moo Lee, Andrew W. Kraft, Michael R. Bruchas
Publikováno v:
Cerebral Cortex (New York, NY)
Brain connectomics has expanded from histological assessment of axonal projection connectivity (APC) to encompass resting state functional connectivity (RS-FC). RS-FC analyses are efficient for whole-brain mapping, but attempts to explain aspects of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee8b1b83a85a41d57fdeb23d25467d40
http://europepmc.org/articles/PMC6057523
http://europepmc.org/articles/PMC6057523