- Mountain View CA, US Neil Birkbeck - Santa Cruz CA, US Mohammad Izadi - San Jose CA, US
International Classification:
G06T 5/00
Abstract:
Processing a spherical video using denoising is described. Video content comprising the spherical video is received. Whether a camera geometry or a map projection, or both, used to generate the spherical video is available is then determined. The spherical video is denoised using a first technique responsive to a determination that the camera geometry, the map projection, or both is available. Otherwise, the spherical video is denoised using a second technique. At least some steps of the second technique can be different from steps of the first technique. The denoised spherical video can be encoded for transmission or storage using less data than encoding the spherical video without denoising.
Method For Denoising Omnidirectional Videos And Rectified Videos
- Mountain View CA, US Neil Birkbeck - Santa Cruz CA, US Mohammad Izadi - San Jose CA, US
Assignee:
Google LLC - Mountain View CA
International Classification:
G06T 5/00
Abstract:
A method for denoising video content includes identifying a first frame block associated with a first frame of the video content. The method also includes estimating a first noise model that represents characteristics of the first frame block. The method also includes identifying at least one frame block adjacent to the first frame block. The method also includes generating a second noise model that represents characteristics of the at least one frame block adjacent to the first frame block by adjusting the first noise model based on at least one characteristic of the at least one frame block adjacent to the first frame block. The method also includes denoising the at least one frame block adjacent to the first frame block using the second noise model.
- Plano TX, US Neil Birkbeck - Santa Cruz CA, US Giorgio Di Guardia - Plainsboro NJ, US Jingdan Zhang - Bellevue WA, US Michal Sofka - Franklin Park NJ, US James B. Thompson - St. Charles IL, US Gianluca Paladini - Skillman NJ, US
International Classification:
G06F 19/00
Abstract:
A method for processing medical imaging data includes: (a) selecting a subset of medical imaging data to be processed, wherein the medical imaging data is stored in a cloud-based storage system; (b) choosing a processing algorithm to apply to the selected subset of medical imaging data, wherein the chosen processing algorithm is stored in the cloud-based storage system; (c) executing the chosen processing algorithm in the cloud-based storage system to generate a processing result; and (d) displaying the processing result to a client via a user interface. Systems for processing medical imaging data are described.
Method And System For Segmentation Of The Liver In Magnetic Resonance Images Using Multi-Channel Features
- Munich, DE Neil Birkbeck - Sunnyvale CA, US Zhang Jingdan - Bellevue WA, US Shaohua Kevin Zhou - Plainsboro NJ, US
Assignee:
Siemens Aktiengesellschaft - Munich
International Classification:
G06T 7/00 G06K 9/62
Abstract:
A method and system for fully automatic liver segmentation in a multi-channel magnetic resonance (MR) image is disclosed. An initial liver boundary in the multi-channel MR image, such as an MR Dixon scan. The segmented initial liver boundary in the multi-channel MR image is refined based on features extracted from multiple channels of the multi-channel MR image using a trained boundary detector. The features may be extracted from an opposed channel and a water channel of an MR Dixon scan.
Multi-Bone Segmentation For 3D Computed Tomography
Dijia Wu - North Brunswick NJ, US Neil Birkbeck - Sunnyvale CA, US Michal Sofka - Franklin Park NJ, US Meizhu Liu - Plainsboro NJ, US Shaohua Kevin Zhou - Plainsboro NJ, US
Assignee:
SIEMENS PRODUCT LIFECYCLE MANAGEMENT SOFTWARE INC. - Plano TX
International Classification:
G06T 7/00
US Classification:
382131
Abstract:
Multiple object segmentation is performed for three-dimensional computed tomography. The adjacent objects are individually segmented. Overlapping regions or locations designated as belonging to both objects may be identified. Confidence maps for the individual segmentations are used to label the locations of the overlap as belonging to one or the other object, not both. This re-segmentation is applied for the overlapping local, and not other locations. Confidence maps in re-segmentation and application just to overlap locations may be used independently of each other or in combination.
Google
Software Engineer
Siemens Jul 1, 2011 - Mar 1, 2013
Research Scientist
University of Alberta Jan 2005 - Feb 2005
Research Assistant
University of Alberta Sep 2003 - Nov 2003
Teaching Assistant
University of Alberta Computer Vision Lab Jun 2003 - Sep 2003
Research Assistant
Education:
University of Alberta 2005 - 2011
Doctorates, Doctor of Philosophy
University of Alberta 2003 - 2005
Master of Science, Masters
University of Alberta 1999 - 2003
Bachelors, Bachelor of Science
Jasper Place Composite High School
Skills:
C++ Matlab Computer Science Computer Graphics Machine Learning Python Java Opengl Computer Vision Algorithms Linux Image Processing Qt Cuda Software Engineering C Latex Bash Artificial Intelligence Php Pattern Recognition Data Mining Sql Numerical Analysis Programming Distributed Systems Signal Processing Mathematical Modeling Optimization Parallel Programming Software Development Software Design Subversion Object Oriented Design Image Analysis Parallel Computing Scientific Computing Modeling High Performance Computing Image Segmentation Visual Studio Simulations Algorithm Design Medical Imaging Digital Image Processing
Interests:
C/C Computer Graphics Linux Computer Vision Opengl Software Engineering
Youtube
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17 Apr, 2010
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Duration:
3m 25s
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Education
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28 May, 2012
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2m 48s
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Category:
Education
Uploaded:
29 Sep, 2012
Duration:
33m 31s
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Category:
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14 Sep, 2012
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Category:
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Uploaded:
17 Feb, 2011
Duration:
11m 8s
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Duration:
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