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Allen R Tannenbaum

from Stony Brook, NY
Age ~71
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Also known as:
Allen Rina Tannenbaum, Allen Irena Tannenbaum, Alan Tannenbaum, Robert Tannenbaum Allen, Tannebaum Allen
Related to:
Fredric D Tannenbaum, 68Anita M Tannenbaum, 67Alan J Tannenbaum, 68A Tannenbaum, 68Steven R Tannenbaum, 87R Tannenbaum, 70Sarah R Tannenbaum, 42Carol E Tannenbaum, 85Rina Tannenbaum, 70Mark S Tannenbaum, 57Emmanuel David Tannenbaum, 46Irena Tannenbaum, 71
Connected to:
Barbara J Tannenbaum, 68Daniel Tannen, 33Dean M Tannenbaum, 46Edward Tannenbaum, 97Felix B Tannenbaum, 49Gary P Tannen, 72Gina A Tannenbaum, 61Jacqueline M Tannenbaum, 73Jamie B Tannenbaum, 62John H Tannenberg, 77

Allen Tannenbaum Phones & Addresses

  • Stony Brook, NY
  • 201 50Th Ave APT 21B, Long Is City, NY 11101 (646) 673-4956
  • Long Island City, NY
  • 4214 Cheltingham Ln SE, Smyrna, GA 30082 (770) 319-7995
  • Minneapolis, MN
  • Edina, MN
  • Framingham, MA
  • Gainesville, FL

Work

Company: Comprehensive cancer center/ece uab Jan 2012 to Aug 2013 Position: Bunn professor

Education

Degree: Doctorates, Doctor of Philosophy School / High School: Harvard University 1973 to 1976 Specialities: Mathematics

Skills

Applied Mathematics • Biomedical Engineering • Control Theory • Computer Vision • Image Processing • Pattern Recognition • Machine Learning • Signal Processing • Algorithms • Latex • Science • Computer Science • Optimization • Image Analysis • Scientific Computing • Medical Imaging • Mathematical Modeling • Physics • Statistics • Bioinformatics • Numerical Analysis • Mathematica • Data Mining • University Teaching • Artificial Intelligence • Simulations • Matlab • Simulink • R • Algorithm Design • C++ • High Performance Computing • Parallel Computing • Programming • Theory • Optics • Fortran • Image Segmentation • Python • Neuroscience • Statistical Modeling • Nanotechnology • Computational Biology • Experimentation

Languages

Hebrew • German

Interests

Systems and Control • Applied Mathematics • Medical Imaging

Industries

Biotechnology

Resumes

Resumes

Allen Tannenbaum Photo 1

Distinguished Professor Of Computer Science And Applied Mathematics And Statistics

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Location:
1335 Bold Forbes Dr, Grand Prairie, TX 75052
Industry:
Biotechnology
Work:
Comprehensive Cancer Center/Ece Uab Jan 2012 - Aug 2013
Bunn Professor
Stony Brook University Jan 2012 - Aug 2013
Distinguished Professor of Computer Science and Applied Mathematics and Statistics
The University of Alabama at Birmingham Jan 2012 - Jul 2013
Interim Chair of Electrical and Computer Engineering, Uab
Boston University Jul 2011 - 2012
Visiting Professor
University of Minnesota Aug 1986 - Aug 1999
Professor
Education:
Harvard University 1973 - 1976
Doctorates, Doctor of Philosophy, Mathematics Columbia University 1969 - 1973
Bachelors, Bachelor of Arts, Mathematics
Skills:
Applied Mathematics
Biomedical Engineering
Control Theory
Computer Vision
Image Processing
Pattern Recognition
Machine Learning
Signal Processing
Algorithms
Latex
Science
Computer Science
Optimization
Image Analysis
Scientific Computing
Medical Imaging
Mathematical Modeling
Physics
Statistics
Bioinformatics
Numerical Analysis
Mathematica
Data Mining
University Teaching
Artificial Intelligence
Simulations
Matlab
Simulink
R
Algorithm Design
C++
High Performance Computing
Parallel Computing
Programming
Theory
Optics
Fortran
Image Segmentation
Python
Neuroscience
Statistical Modeling
Nanotechnology
Computational Biology
Experimentation
Interests:
Systems and Control
Applied Mathematics
Medical Imaging
Languages:
Hebrew
German
Allen Tannenbaum Photo 2

Allen Robert Tannenbaum

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Allen Tannenbaum Photo 3

Allen Robert Tannenbaum

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Allen Tannenbaum Photo 4

Allen Robert Tannenbaum

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Business Records

Name / Title
Company / Classification
Phones & Addresses
Allen Tannenbaum
Principal
Allen Tannenbaum, Consultant
Consulting
4214 Cheltingham Ln SE, Smyrna, GA 30082

Publications

Us Patents

Curvature Based System For The Segmentation And Analysis Of Cardiac Magnetic Resonance Images

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US Patent:
6535623, Mar 18, 2003
Filed:
Apr 15, 1999
Appl. No.:
09/293481
Inventors:
Allen Robert Tannenbaum - Edina MN, 55435
International Classification:
G06K 900
US Classification:
382128
Abstract:
A technique for analyzing images for a feature of interest includes generating gradients for a characteristic of an image, such as pixel intensity value, initiating a candidate boundary, and evolving the candidate boundary to the feature boundary. The gradients may be calculated at the outset and referred to through iterative steps in expansion of the candidate boundary to improve the computational efficiency of the processing. The candidate boundary is evolved, either inwardly or outwardly, by reference to mean curvature-weighted normals, resulting in very rapid convergence on the feature. The evolution naturally stops at the feature. The gradients may be diffused or smoothed to improve tolerance to artifacts and noise, while preserving original image definition and resolution. In a cardiac application, the endocardial and myocardial boundaries are analyzed and used to generate composite images, calculate ejection fractions and so forth. The technique is also applicable to other static and dynamic structures and tissues.

Apparatus For Producing A Flattening Map Of A Digitized Image For Conformally Mapping Onto A Surface And Associated Method

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US Patent:
6697538, Feb 24, 2004
Filed:
Jul 28, 2000
Appl. No.:
09/627512
Inventors:
Sigurd B. Angenent - Madison WI
Allen R. Tannenbaum - Smyrna GA
Steven Haker - New Haven CT
Ron Kikinis - Brookline MA
Assignee:
Wisconsin Alumni Research Foundation - Madison WI
International Classification:
G06K 936
US Classification:
382285, 708270, 716 20
Abstract:
A computerized apparatus and associated method and program code on a storage medium, for producing a flattening map of a digitized image. This image may be initially synthetically produced as discrete data or as quasi-discrete image data of a real objectâand the original image data may be stored as two-, three-, or four-dimensional dynamic coordinate data. Once produced, the flattening map can be conformally mapped onto the computer generated surface (whether 2-D, 3-D, or any of the dynamically-varying family of surfaces) for display on a computer-assisted display apparatus in communication with a processor. The apparatus and associated method and program code include constructing a first set of data comprising a plurality of discrete surface-elements to represent at least a portion of a surface of the digitized image, and performing a flattening function on the first set of data to produce the flattening map. The flattening function includes computing, for each discrete surface-element, a solution to each of two systems of linear equations formulated from finding a numerical solution to a selected partial differential equation (PDE), and can be performed on each of a series of data sets changing over time to produce a corresponding series of flattening maps.

Curvature Based System For The Segmentation And Analysis Of Image Data

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US Patent:
6721450, Apr 13, 2004
Filed:
Feb 4, 2003
Appl. No.:
10/357974
Inventors:
Allen Robert Tannenbaum - Edina MN, 55435
International Classification:
G06K 934
US Classification:
382173, 382199
Abstract:
A technique for analyzing images for a feature of interest includes generating gradients for a characteristic of an image, such as pixel intensity value, initiating a candidate boundary, and evolving the candidate boundary to the feature boundary. The gradients may be calculated at the outset and referred to through iterative steps in expansion of the candidate boundary to improve the computational efficiency of the processing. The candidate boundary is evolved, either inwardly or outwardly, by reference to mean curvature-weighted normals, resulting in very rapid convergence on the feature. The evolution naturally stops at the feature. The gradients may be diffused or smoothed to improve tolerance to artifacts and noise, while preserving original image definition and resolution. In a cardiac application, the endocardial and myocardial boundaries are analyzed and used to generate composite images, calculate ejection fractions and so forth. The technique is also applicable to other static and dynamic structures and tissues.

Bayesian Methods For Noise Reduction In Image Processing

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US Patent:
7813581, Oct 12, 2010
Filed:
May 6, 2005
Appl. No.:
11/123445
Inventors:
Ben G. Fitzpatrick - Marina del Rey CA, US
Allen Robert Tannenbaum - Smyrna GA, US
International Classification:
G06K 9/40
G06K 9/00
G06K 9/68
H04N 5/225
US Classification:
382260, 382103, 382254, 382263, 382278, 348169
Abstract:
Improved methodology for image processing and object tracking that, inter alia, reduces noise. In one embodiment, the methodology is applied to moving targets, and comprises processing sequences of images that have been corrupted by one or more noise sources (e. g. , sensor noise, medium noise, and/or target reflection noise). A likelihood or similar logical construct (e. g. , Bayes' rule) is applied to the individual images (or aggregations thereof) of an image sequence in order to generate a posterior image for each observed image. The posterior images are fed-forward to the determination of the posterior image for one or more subsequent images (after smoothing), thereby making these subsequent determinations more accurate. The net result is a more accurate and noise-reduced representation (and location) of the target in each image.

4D Kappa5 Gaussian Noise Reduction

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US Patent:
62048538, Mar 20, 2001
Filed:
Apr 9, 1998
Appl. No.:
9/058100
Inventors:
Harvey Ellis Cline - Schenectady NY
Allen Robert Tannenbaum - Edina MN
Assignee:
General Electric Company - Schenectady NY
International Classification:
G06K 940
US Classification:
345424
Abstract:
The noise reduction system of the present invention takes into account that noise is random with little between images adjacent physically, and in sampling time. It also takes into account the fact that some sequences of images are cyclical and "wrap around" in time with the beginning closely resembling the end of the cycle. A filter was developed which would smooth noise in a direction along an edge, but will not blur across an edge. It operates by determining vectors tangential to a surface point p, at a current voxel, and projecting 4D data onto the tangential vectors. A curvature matrix B. sub. alpha. beta. is determined. The eigenvalues of curvature matrix B. sub. alpha. beta. are determined to result in three curvatures for 4 dimensions. If the sign of all of the eigenvalues is the same, the current voxel is filtered, else, it is unchanged.

Isbn (Books And Publications)

Feedback Control Theory

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Author

Allen R. Tannenbaum

ISBN #

0023300116

Feedback Control, Nonlinear Systems, and Complexity

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Author

Allen R. Tannenbaum

ISBN #

3540199438

Robust Control of Infinite Dimensional Systems : Frequency Domain Methods

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Author

Allen R. Tannenbaum

ISBN #

3540199942

Mathematical Methods in Computer Vision

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Author

Allen Tannenbaum

ISBN #

0387004971

John & Yoko: A New York Love Story

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Author

Allen Tannenbaum

ISBN #

1933784229

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Allen R Tannenbaum from Stony Brook, NY, age ~71 Get Report