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A L Yuille

Showing results (11-20 of 20) with videos related to

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Neural Computation|August 16, 2002
Bayesian A* tree search with expected O(N) node expansions: applications to road trackingJames M Coughlan, A L Yuille
Neural Computation|June 14, 2003
Manhattan world: orientation and outlier detection by Bayesian inferenceJames M Coughlan, A L Yuille
Vision Research|March 1, 1993
Dependence of speed and direction perception on cinematogram dot densityS N Watamaniuk, N M Grzywacz, A L Yuille
Biological Cybernetics|January 1, 1989
Quadrature and the development of orientation selective cortical cells by Hebb rulesA L Yuille, D M Kammen, D S Cohen
Neural Computation|August 23, 2000
Probabilistic motion estimation based on temporal coherenceP Y Burgi, A L Yuille, N M Grzywacz
Neuroimage|August 29, 2009
Classification of spatially unaligned fMRI scansAriana Anderson, Ivo D Dinov, Jonathan E Sherin, et al.
Diagnostic and Interventional Imaging|October 21, 2019
Deep lessons learned: Radiology, oncology, pathology, and computer science experts unite around artificial intelligence to strive for earlier pancreatic cancer diagnosisE M Weisberg, L C Chu, S Park, et al.
Diagnostic and Interventional Imaging|May 25, 2020
Erratum to "Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation" [Diagn. Interv. Imaging. 101 (2020) 35-44]S Park, L C Chu, E K Fishman, et al.
Diagnostic and Interventional Imaging|July 31, 2019
Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementationS Park, L C Chu, E K Fishman, et al.
Diagnostic and Interventional Imaging|April 13, 2020
Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics featuresS Park, L C Chu, R H Hruban, et al.
Pageof 2

Showing results (11-20 of 20) with videos related to

Sort By:
Pageof 2
You have reached the last page of results.This site can display upto 20 results.
Neural Computation|August 16, 2002
Bayesian A* tree search with expected O(N) node expansions: applications to road trackingJames M Coughlan, A L Yuille
Neural Computation|June 14, 2003
Manhattan world: orientation and outlier detection by Bayesian inferenceJames M Coughlan, A L Yuille
Vision Research|March 1, 1993
Dependence of speed and direction perception on cinematogram dot densityS N Watamaniuk, N M Grzywacz, A L Yuille
Biological Cybernetics|January 1, 1989
Quadrature and the development of orientation selective cortical cells by Hebb rulesA L Yuille, D M Kammen, D S Cohen
Neural Computation|August 23, 2000
Probabilistic motion estimation based on temporal coherenceP Y Burgi, A L Yuille, N M Grzywacz
Neuroimage|August 29, 2009
Classification of spatially unaligned fMRI scansAriana Anderson, Ivo D Dinov, Jonathan E Sherin, et al.
Diagnostic and Interventional Imaging|October 21, 2019
Deep lessons learned: Radiology, oncology, pathology, and computer science experts unite around artificial intelligence to strive for earlier pancreatic cancer diagnosisE M Weisberg, L C Chu, S Park, et al.
Diagnostic and Interventional Imaging|May 25, 2020
Erratum to "Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation" [Diagn. Interv. Imaging. 101 (2020) 35-44]S Park, L C Chu, E K Fishman, et al.
Diagnostic and Interventional Imaging|July 31, 2019
Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementationS Park, L C Chu, E K Fishman, et al.
Diagnostic and Interventional Imaging|April 13, 2020
Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics featuresS Park, L C Chu, R H Hruban, et al.
Pageof 2