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Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation

Diagnostic and interventional imaging, 2020-01, Vol.101 (1), p.35-44

2020

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Deep lessons learned: Radiology, oncology, pathology, and computer science experts unite around artificial intelligence to strive for earlier pancreatic cancer diagnosis

Diagnostic and interventional imaging, 2020-02, Vol.101 (2), p.111-115

2020

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Statistical edge detection: learning and evaluating edge cues

IEEE transactions on pattern analysis and machine intelligence, 2003-01, Vol.25 (1), p.57-74

2003

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Automated Extraction of the Cortical Sulci Based on a Supervised Learning Approach

IEEE transactions on medical imaging, 2007-04, Vol.26 (4), p.541-552

2007

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Fundamental limits of Bayesian inference: order parameters and phase transitions for road tracking

IEEE transactions on pattern analysis and machine intelligence, 2000-02, Vol.22 (2), p.160-173

2000

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Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

Diagnostic and interventional imaging, 2020-09, Vol.101 (9), p.555-564

2020

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Guest Editorial: Geometry, Lighting, Motion, and Learning

International journal of computer vision, 2014-04, Vol.107 (2), p.99-100

2014

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Computer vision needs a core and foundations

Image and vision computing, 2012-08, Vol.30 (8), p.469-471

Yuille, A.L.

2012

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The generic viewpoint assumption and planar bias

IEEE transactions on pattern analysis and machine intelligence, 2003-06, Vol.25 (6), p.775-778

2003

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Classification of spatially unaligned fMRI scans

NeuroImage (Orlando, Fla.), 2010-02, Vol.49 (3), p.2509-2519

2010

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Detecting and reading text in natural scenes

Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, 2004, Vol.2, p.II-II

2004

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Erratum to “Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation” [Diagn. Interv. Imaging. 101 (2020) 35–44]

Diagnostic and interventional imaging, 2020-06, Vol.101 (6), p.427-427

2020

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Statistical cues for domain specific image segmentation with performance analysis

Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2000, Vol.1, p.125-132 vol.1

2000

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Detecting Object Boundaries Using Low-, Mid-, and High-level Information

2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007, p.1-8

2007

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The invisible hand algorithm: Solving the assignment problem with statistical physics

Neural networks, 1994, Vol.7 (3), p.477-490

1994

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Fundamental bounds on edge detection: an information theoretic evaluation of different edge cues

Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, Vol.1, p.573-579 Vol. 1

1999

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High-level and generic models for visual search: When does high level knowledge help?

Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, Vol.2, p.631-637 Vol. 2

1999

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An A ∗ perspective on deterministic optimization for deformable templates

Pattern recognition, 2000, Vol.33 (4), p.603-616

2000

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Manhattan World: compass direction from a single image by Bayesian inference

Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, Vol.2, p.941-947 vol.2

1999

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Order parameters for minimax entropy distributions: when does high level knowledge help?

Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2000, Vol.1, p.558-565 vol.1

2000

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Feature extraction from faces using deformable templates

Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1989, p.104-109

1989

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The bas-relief ambiguity

Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1997, p.1060-1066

1997

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Impossible shaded images

IEEE transactions on pattern analysis and machine intelligence, 1993-02, Vol.15 (2), p.166-170

1993

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A Time-Efficient Cascade for Real-Time Object Detection: With applications for the visually impaired

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops, 2005, p.28-28

2005

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Dimension reduction, generalized deformable models and the development of ocularity and orientation

Neural networks, 1996, Vol.9 (2), p.309-319

1996

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Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation

Deep lessons learned: Radiology, oncology, pathology, and computer science experts unite around artificial intelligence to strive for earlier pancreatic cancer diagnosis

Statistical edge detection: learning and evaluating edge cues

Automated Extraction of the Cortical Sulci Based on a Supervised Learning Approach

Fundamental limits of Bayesian inference: order parameters and phase transitions for road tracking

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

Guest Editorial: Geometry, Lighting, Motion, and Learning

Computer vision needs a core and foundations

The generic viewpoint assumption and planar bias

Classification of spatially unaligned fMRI scans

Detecting and reading text in natural scenes

Erratum to “Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation” [Diagn. Interv. Imaging. 101 (2020) 35–44]

Statistical cues for domain specific image segmentation with performance analysis

Detecting Object Boundaries Using Low-, Mid-, and High-level Information

The invisible hand algorithm: Solving the assignment problem with statistical physics

Fundamental bounds on edge detection: an information theoretic evaluation of different edge cues

High-level and generic models for visual search: When does high level knowledge help?

An A ∗ perspective on deterministic optimization for deformable templates

Manhattan World: compass direction from a single image by Bayesian inference

Order parameters for minimax entropy distributions: when does high level knowledge help?

Feature extraction from faces using deformable templates

The bas-relief ambiguity

Impossible shaded images

A Time-Efficient Cascade for Real-Time Object Detection: With applications for the visually impaired

Dimension reduction, generalized deformable models and the development of ocularity and orientation

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