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Richard S Szeliski

from Bellevue, WA
Age ~67
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Also known as:
Richard S Mccoy, Dick S Szeliski, Rick S Szeliski, Richar Szeliski
Phone and address:
2602 131St Pl NE, Bellevue, WA 98005
(425) 882-7359
Related to:
Stephen Szeliski, 30
Connected to:
Thomas J Szelistowski, 64Klara Szell, 65Roger W Szelmeczka, 63Catherine E Szeltner, 34Mark P Szeltner, 39Lisa M Szem, 63Bruce L Szember, 61Carol A Szemborski, 75David S Szemborski, 85George J Szemborski, 59

Richard Szeliski Phones & Addresses

  • 2602 131St Pl NE, Bellevue, WA 98005 (425) 882-7359
  • 16559 Shore St, Leavenworth, WA 98826 (509) 763-2068
  • Arlington, MA
  • Redmond, WA
  • Roxbury, MA
  • 2602 131St Pl NE, Bellevue, WA 98005 (206) 391-0166

Work

Company: Facebook 2017 Position: Research scientist

Education

Degree: High school graduate or higher

Industries

Internet

Resumes

Resumes

Richard Szeliski Photo 1

Research Scientist

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Location:
Bellevue, WA
Industry:
Internet
Work:
Facebook
Research Scientist
Microsoft 1995 - 2015
Distinguished Scientist and Research Manager

Publications

Amazon

Image Alignment And Stitching (Foundations And Trends(R) In Computer Graphics And Vision)

IMAGE ALIGNMENT AND STITCHING (Foundations and Trends(r) in Computer Graphics and Vision)

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Image Alignment and Stitching: A Tutorial reviews image alignment and image stitching algorithms. Image alignment algorithms can discover the correspondence relationships among images with varying degrees of overlap. They are ideally suited for applications such as video stabilization, summarization...
Author

Richard Szeliski

Binding

Paperback

Pages

120

Publisher

Now Publishers Inc

ISBN #

1933019042

EAN Code

9781933019048

ISBN #

5

Bayesian Modeling Of Uncertainty In Low-Level Vision (The Springer International Series In Engineering And Computer Science)

Bayesian Modeling of Uncertainty in Low-Level Vision (The Springer International Series in Engineering and Computer Science)

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Vision has to deal with uncertainty. The sensors are noisy, the prior knowledge is uncertain or inaccurate, and the problems of recovering scene information from images are often ill-posed or underconstrained. This research monograph, which is based on Richard Szeliski's Ph.D. dissertation at Carneg...
Author

Richard Szeliski

Binding

Paperback

Pages

198

Publisher

Springer

ISBN #

1461289041

EAN Code

9781461289043

ISBN #

4

Bayesian Modeling Of Uncertainty In Low-Level Vision (The Springer International Series In Engineering And Computer Science)

Bayesian Modeling of Uncertainty in Low-Level Vision (The Springer International Series in Engineering and Computer Science)

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Vision has to deal with uncertainty. The sensors are noisy, the prior knowledge is uncertain or inaccurate, and the problems of recovering scene information from images are often ill-posed or underconstrained. This research monograph, which is based on Richard Szeliski's Ph.D. dissertation at Carneg...
Author

Richard Szeliski

Binding

Hardcover

Pages

198

Publisher

Springer

ISBN #

0792390393

EAN Code

9780792390398

ISBN #

2

Computer Vision: Algorithms And Applications (Texts In Computer Science) By Szeliski, Richard 2011 Edition [Hardcover(2010)]

Computer Vision: Algorithms and Applications (Texts in Computer Science) by Szeliski, Richard 2011 edition [Hardcover(2010)]

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Binding

Hardcover

Publisher

Springer

ISBN #

10

[ [ [ Computer Vision: Algorithms And Applications[ Computer Vision: Algorithms And Applications ] By Szeliski, Richard ( Author )Nov-24-2010 Hardcover

[ [ [ Computer Vision: Algorithms and Applications[ COMPUTER VISION: ALGORITHMS AND APPLICATIONS ] By Szeliski, Richard ( Author )Nov-24-2010 Hardcover

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Author

Richard Szeliski

Binding

Hardcover

Publisher

Springer

EAN Code

8601400076811

ISBN #

9

Computer Vision: Algorithms And Applications (Texts In Computer Science) 2011 Edition By Szeliski, Richard Published By Springer (2010)

Computer Vision: Algorithms and Applications (Texts in Computer Science) 2011 Edition by Szeliski, Richard published by Springer (2010)

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Binding

Hardcover

Publisher

Springer

ISBN #

8

Richard Szeliski'scomputer Vision: Algorithms And Applications (Texts In Computer Science) [Hardcover](2010)

Richard Szeliski'scomputer Vision: Algorithms and Applications (Texts in Computer Science) [Hardcover](2010)

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Author

Richard Szeliski (Author)

Binding

Hardcover

Publisher

Springer

EAN Code

8601401759218

ISBN #

7

By Richard Szeliski - Computer Vision: Algorithms And Applications (Texts In Computer Science) (9/19/10)

By Richard Szeliski - Computer Vision: Algorithms and Applications (Texts in Computer Science) (9/19/10)

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Author

Richard Szeliski

Binding

Hardcover

Publisher

Springer

ISBN #

6

Us Patents

Stereo Reconstruction Employing A Layered Approach

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US Patent:
6348918, Feb 19, 2002
Filed:
Mar 20, 1998
Appl. No.:
09/045519
Inventors:
Richard S. Szeliski - Bellevue WA
Simon Baker - New York NY
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06T 1500
US Classification:
345419
Abstract:
A system and method for extracting structure from stereo that represents the scene as a collection of planar layers. Each layer optimally has an explicit 3D plane equation, a colored image with per-pixel opacity, and a per-pixel depth value relative to the plane. Initial estimates of the layers are recovered using techniques from parametric motion estimation. The combination of a global model (the plane) with a local correction to it (the per-pixel relative depth value) imposes enough local consistency to allow the recovery of shape in both textured and untextured regions.

Inverse Texture Mapping Using Weighted Pyramid Blending

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US Patent:
6469710, Oct 22, 2002
Filed:
Sep 25, 1998
Appl. No.:
09/160311
Inventors:
Richard S. Szeliski - Bellevue WA
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G09G 500
US Classification:
345619, 345629, 382284
Abstract:
A system and method for inverse texture mapping in which given a 3D model and several images from different viewpoints, a texture map is extracted for each planar surface in the 3D model. The system and method employs a unique weighted pyramid feathering scheme for blending multiple images to form the texture map, even where the images are taken from different viewpoints, at different scales, and with different exposures. This scheme also makes it possible to blend images with cut-out regions which may be present due to occlusions or moving objects. It further advantageously employs weight maps to improve the quality of the blended image.

Multi-View Approach To Motion And Stereo

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US Patent:
6487304, Nov 26, 2002
Filed:
Jun 16, 1999
Appl. No.:
09/334857
Inventors:
Richard S. Szeliski - Bellevue WA
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06K 900
US Classification:
382107, 382167
Abstract:
A system and process for computing motion or depth estimates from multiple images. In general terms this is accomplished by associating a depth or motion map with each input image (or some subset of the images equal or greater than two), rather that computing a single map for all the images. In addition, consistency between the estimates associated with different images is ensured. More particularly, this involves minimizing a three-part cost function, which consists of an intensity (or color) compatibility constraint, a motion/depth compatibility constraint, and a flow smoothness constraint. In addition, a visibility term is added to the intensity (or color) compatibility and motion/depth compatibility constraints to prevent the matching of pixels into areas that are occluded. In operation, the cost function is computed in two phases. During an initializing phase, the motion or depth for each image being examined are estimated independently.

System And Process For Viewing Panoramic Video

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US Patent:
6559846, May 6, 2003
Filed:
Jul 7, 2000
Appl. No.:
09/611987
Inventors:
Matthew T. Uyttendaele - Seattle WA
Richard S. Szeliski - Bellevue WA
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06T 1700
US Classification:
345473
Abstract:
The primary components of the panoramic video viewer include a decoder module. The purpose of the decoder module is to input incoming encoded panoramic video data and to output a decoded version thereof. The incoming data may be provided over a network and originate from a server, or it may simply be read from a storage media, such as a hard drive, CD or DVD. Once decoded, the data associated with each video frame is preferably stored in a storage module and made available to a 3D rendering module. The 3D rendering module is essentially a texture mapper that takes the frame data and maps the desired views onto a prescribed environment model. The output of the 3D rendering module is provided to a display module where the panoramic video is viewed by a user of the system. Typically, the user will be viewing just a portion of the scene depicted in the panoramic video at any one time, and will be able to control what portion is viewed. Preferably, the panoramic video viewer will allow the user to pan through the scene to the left, right, up or down.

Video-Based Rendering With User-Controlled Movement

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US Patent:
6600491, Jul 29, 2003
Filed:
Aug 22, 2000
Appl. No.:
09/643782
Inventors:
Richard S. Szeliski - Bellevue WA
David Salesin - Seattle WA
Arno Schödl - Berlin, DE
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06T 1570
US Classification:
345473, 345474, 345422, 345421, 345723, 348700
Abstract:
A system and process for generating a video animation from the frames of a video sprite with user-controlled motion is presented. An object is extracted from the frames of an input video and processed to generate a new video sequence or video sprite of that object. In addition, the translation velocity of the object for each frame is computed and associated with each frame in the newly generated video sprite. The system user causes a desired path to be generated for the object featured in the video sprite to follow in the video animation. Frames of the video sprite showing the object of interest are selected and inserted in a background image, or frame of a background video, along the prescribed path. The video sprite frames are selected by comparing a last-selected frame to the other video sprite frames, and selecting a video sprite frame that is identified in the comparison as corresponding to an acceptable transition from the last-selected frame. Each newly selected video sprite frame is inserted at a point along the prescribed path dictated by the velocity associated with the object in the last-inserted frame.

System And Process For Generating 3D Video Textures Using Video-Based Rendering Techniques

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US Patent:
6611268, Aug 26, 2003
Filed:
Aug 22, 2000
Appl. No.:
09/643635
Inventors:
Richard S. Szeliski - Bellevue WA
David Salesin - Seattle WA
Arno Schödl - Berlin, DE
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06T 1570
US Classification:
345473, 345420, 345422, 345474, 345723, 345683, 348700
Abstract:
A system and process for generating a 3D video animation of an object referred to as a 3D Video Texture is presented. The 3D Video Texture is constructed by first simultaneously videotaping an object from two or more different cameras positioned at different locations. Video from, one of the cameras is used to extract, analyze and synthesize a video sprite of the object of interest. In addition, the first, contemporaneous, frames captured by at least two of the cameras are used to estimate a 3D depth map of the scene. The background of the scene contained within the depth map is then masked out, and a clear shot of the scene background taken before filming of the object began, leaving just the object. To generate each new frame in the 3D video animation, the extracted region making up a âframeâ of the video sprite is mapped onto the previously generated 3D surface. The-resulting image is rendered from a novel viewpoint, and then combined with a flat image of the background which has been warped to the correct location.

Video-Based Rendering

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US Patent:
6636220, Oct 21, 2003
Filed:
May 30, 2000
Appl. No.:
09/583313
Inventors:
Richard S. Szeliski - Bellevue WA
David Salesin - Seattle WA
Arno Schödl - Berlin, DE
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06T 1570
US Classification:
345475, 345723, 348700
Abstract:
A system and process for generating a new video sequence from frames taken from an input video clip. Generally, this involves computing a similarity value between each of the frames of the input video clip and each of the other frames. For each frame, the similarity values associated therewith are analyzed to identify potentially acceptable transitions between it and the remaining frames. A transition is considered acceptable if it would appear smooth to a person viewing a video containing the frames, or at least if the transition is one of the best available. A new video sequence is then synthesized using the identified transitions to specify an order in which the frames associated with these transitions are to be played. Finally, the new video sequence is rendered by playing the frames of the input video clip in the order specified in the synthesizing procedure. This rendering procedure can include a smoothing action in which those transitions that were deemed acceptable, but would not appear smooth to a viewer, are smoothed to lessen the discontinuity.

Stereo Reconstruction From Multiperspective Panoramas

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US Patent:
6639596, Oct 28, 2003
Filed:
Sep 20, 1999
Appl. No.:
09/399426
Inventors:
Richard S. Szeliski - Bellevue WA
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06T 1500
US Classification:
345427
Abstract:
A system and process for computing a 3D reconstruction of a scene using multiperspective panoramas. The reconstruction can be generated using a cylindrical sweeping approach, or under some conditions, traditional stereo matching algorithms. The cylindrical sweeping process involves projecting each pixel of the multiperspective panoramas onto each of a series of cylindrical surfaces of progressively increasing radii. For each pixel location on each cylindrical surface, a fitness metric is computed for all the pixels projected thereon to provide an indication of how closely a prescribed characteristic of the projected pixels matches. Then, for each respective group of corresponding pixel locations of the cylindrical surfaces, it is determined which location has a fitness metric that indicates the prescribed characteristic of the projected pixels matches more closely than the rest. For each of these winning pixel locations, its panoramic coordinates are designated as the position of the portion of the scene depicted by the pixels projected to that location. Additionally, in some cases a sufficiently horizontal epipolar geometry exists between multiperspective panoramas such that traditional stereo matching algorithms can be employed for the reconstruction.

Isbn (Books And Publications)

Bayesian Modeling of Uncertainty in Low-Level Vision

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Author

Richard Szeliski

ISBN #

0792390393

Vision Algorithms: Theory and Practice International Workshop on Vision Algorithms, Corfu, Greece, September 21-22, 1999 Proceedings

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Author

Richard Szeliski

ISBN #

3540679731

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Richard S Szeliski from Bellevue, WA, age ~67 Get Report