new TOP 200 Companies filing patents this week

new Companies with the Most Patent Filings

Jamie Daniel Joseph Shotton patents

Recent patents with Jamie Daniel Joseph Shotton listed as an inventor - additional entries may be under other spellings.


Jamie Daniel Joseph Shotton - Related organizations: Microsoft Technology Licensing, Llc patents, Microsoft Corporation patents, Mirosoft Corporation patents

Gesture recognition techniques

12/22/16 - 20160370867 - In one or more implementations, a static geometry model is generated, from one or more images of a physical environment captured using a camera, using one or more static objects to model corresponding one or more objects in the physical environment. Interaction of a dynamic object with at least one
Inventors: David Kim, Otmar D. Hilliges, Shahram Izadi, Patrick L. Olivier, Jamie Daniel Joseph Shotton, Pushmeet Kohli, David G. Molyneaux, Stephen E. Hodges, Andrew W. Fitzgibbon

Human body pose estimation

06/16/16 - 20160171295 - Techniques for human body pose estimation are disclosed herein. Depth map images from a depth camera may be processed to calculate a probability that each pixel of the depth map is associated with one or more segments or body parts of a body. Body parts may then be constructed of
Inventors: Jamie Daniel Joseph Shotton, Andrew William Fitzgibbon

Reducing interference between multiple infra-red depth cameras

06/09/16 - 20160163054 - Systems and methods for reducing interference between multiple infra-red depth cameras are described. In an embodiment, the system comprises multiple infra-red sources, each of which projects a structured light pattern into the environment. A controller is used to control the sources in order to reduce the interference caused by overlapping
Inventors: Shahram Izadi, David Molyneaux, Otmar Hilliges, David Kim, Jamie Daniel Joseph Shotton, Stephen Edward Hodges, David Alexander Butler, Andrew Fitzgibbon, Pushmeet Kohli

Model fitting from raw time-of-flight images

05/05/16 - 20160127715 - Model fitting from raw time of flight image data is described, for example, to track position and orientation of a human hand or other entity. In various examples, raw image data depicting the entity is received from a time of flight camera. A 3D model of the entity is accessed
Inventors: Jamie Daniel Joseph Shotton, Toby Sharp, Jonathan James Taylor, Pushmeet Kohli, Shahram Izadi, Andrew William Fitzgibbon, Reinhard Sebastian Bernhard Nowozin

Depth from time of flight camera

04/14/16 - 20160104031 - Region of interest detection in raw time of flight images is described. For example, a computing device receives at least one raw image captured for a single frame by a time of flight camera. The raw image depicts one or more objects in an environment of the time of flight
Inventors: Jamie Daniel Joseph Shotton, Cem Keskin, Christoph Rhemann, Toby Sharp, Duncan Paul Robertson, Pushmeet Kohli, Andrew William Fitzgibbon, Shahram Izadi

Tracking hand pose using forearm-hand model

03/24/16 - 20160086349 - Tracking hand pose from image data is described, for example, to control a natural user interface or for augmented reality. In various examples an image is received from a capture device, the image depicting at least one hand in an environment. For example, a hand tracker accesses a 3D model
Inventors: Jamie Daniel Joseph Shotton, Duncan Paul Robertson, Jonathan James Taylor, Cem Keskin, Shahram Izadi, Andrew William Fitzgibbon

Pose tracker with multi threaded architecture

03/24/16 - 20160086025 - Tracking pose of an articulated entity from image data is described, for example, to control a game system, natural user interface or for augmented reality. In various examples a plurality of threads execute on a parallel computing unit, each thread processing data from an individual frame of a plurality of
Inventors: Jamie Daniel Joseph Shotton, Toby Sharp, Duncan Paul Robertson, Andrew William Fitzgibbon

Tracking hand/body pose

03/24/16 - 20160085310 - Tracking hand or body pose from image data is described, for example, to control a game system, natural user interface or for augmented reality. In various examples a prediction engine takes a single frame of image data and predicts a distribution over a pose of a hand or body depicted
Inventors: Jamie Daniel Joseph Shotton, Cem Keskin, Jonathan James Taylor, Toby Sharp, Shahram Izadi, Andrew William Fitzgibbon, Pushmeet Kohli, Duncan Paul Robertson

Tracking using sensor data

12/03/15 - 20150347846 - Tracking using sensor data is described, for example, where a plurality of machine learning predictors are used to predict a plurality of complementary, or diverse, parameter values of a process describing how the sensor data arises. In various examples a selector selects which of the predicted values are to be
Inventors: Abner GuzmÁn-rivera, Pushmeet Kohli, Benjamin Michael Glocker, Jamie Daniel Joseph Shotton, Shahram Izadi, Toby Sharp, Andrew William Fitzgibbon

Sensor data filtering

10/15/15 - 20150296152 - Filtering sensor data is described, for example, where filters conditioned on a local appearance of the signal are predicted by a machine learning system, and used to filter the sensor data. In various examples the sensor data is a stream of noisy video image data and the filtering process denoises
Inventors: Sean Ryan Francesco Fanello, Cem Keskin, Pushmeet Kohli, Shahram Izadi, Jamie Daniel Joseph Shotton, Antonio Criminisi

Depth sensing using an rgb camera

09/03/15 - 20150248765 - A method of sensing depth using an RGB camera. In an example method, a color image of a scene is received from an RGB camera. The color image is applied to a trained machine learning component which uses features of the image elements to assign all or some of the
Inventors: Antonio Criminisi, Duncan Paul Robertson, Peter Kontschieder, Pushmeet Kohli, Henrik Turbell, Adriana Dumitras, Indeera Munasinghe, Jamie Daniel Joseph Shotton

Contour-based classification of objects

07/16/15 - 20150199592 - Described herein is a contour-based method of classifying an item, such as a physical object or pattern. In an example method, a one-dimensional (1D) contour signal is received for an object. The one-dimensional contour signal comprises a series of 1D or multi-dimensional data points (e.g. 3D data points) that represent
Inventors: David Kim, Cem Keskin, Jamie Daniel Joseph Shotton, Shahram Izadi

Object detection in optical sensor systems

06/11/15 - 20150160785 - Object detection techniques for use in conjunction with optical sensors is described. In one or more implementations, a plurality of inputs are received, each of the inputs being received from a respective one of a plurality of optical sensors. Each of the plurality of inputs are classified using machine learning
Inventors: Liang Wang, Sing Bing Kang, Jamie Daniel Joseph Shotton, Matheen Siddiqui, Vivek Pradeep, Steven Nabil Bathiche, Luis E. Cabrera-cordon, Pablo Sala

Pose tracking pipeline

05/28/15 - 20150145860 - A method of tracking a subject includes receiving from a source a depth image of a scene including the subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that image the subject and deriving from
Inventors: Robert Matthew Craig, Tommer Leyvand, Craig Peeper, Momin M. Al-ghosien, Matt Bronder, Oliver Williams, Ryan M. Geiss, Jamie Daniel Joseph Shotton, Johnny Lee, Mark Finocchio

Memory facilitation using directed acyclic graphs

05/14/15 - 20150134576 - Memory facilitation using directed acyclic graphs is described, for example, where a plurality of directed acyclic graphs are trained for gesture recognition from human skeletal data, or to estimate human body joint positions from depth images for gesture detection. In various examples directed acyclic graphs are grown during training using
Inventors: Jamie Daniel Joseph Shotton, Toby Sharp, Pushmeet Kohli, Reinhard Sebastian Bernhard Nowozin, John Michael Winn, Antonio Criminisi

Image labeling using geodesic features

10/16/14 - 20140307956 - Image labeling is described, for example, to recognize body organs in a medical image, to label body parts in a depth image of a game player, to label objects in a video of a scene. In various embodiments an automated classifier uses geodesic features of an image, and optionally other
Inventors: Antonio Criminisi, Peter Kontschieder, Pushmeet Kohli, Jamie Daniel Joseph Shotton

Gesture recognition techniques

09/04/14 - 20140247212 - In one or more implementations, a static geometry model is generated, from one or more images of a physical environment captured using a camera, using one or more static objects to model corresponding one or more objects in the physical environment. Interaction of a dynamic object with at least one
Inventors: David Kim, Otmar D. Hilliges, Shahram Izadi, Patrick L. Olivier, Jamie Daniel Joseph Shotton, Pushmeet Kohli, David G. Molyneaux, Stephen E. Hodges, Andrew W. Fitzgibbon

Camera/object pose from predicted coordinates

08/28/14 - 20140241617 - Camera or object pose calculation is described, for example, to relocalize a mobile camera (such as on a smart phone) in a known environment or to compute the pose of an object moving relative to a fixed camera. The pose information is useful for robotics, augmented reality, navigation and other
Inventors: Jamie Daniel Joseph Shotton, Benjamin Michael Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, Andrew William Fitzgibbon

Controlling a computing-based device using hand gestures

07/24/14 - 20140208274 - Methods and system for controlling a computing-based device using both input received from a traditional input device (e.g. keyboard) and hand gestures made on or near a reference object (e.g. keyboard). In some examples, the hand gestures may comprise one or more hand touch gestures and/or one or more hand
Inventors: Samuel Gavin Smyth, Peter John Ansell, Christopher Jozef O'prey, Mitchel Alan Goldberg, Jamie Daniel Joseph Shotton, Toby Sharp, Shahram Izadi, Abigail Jane Sellen, Richard Malcolm Banks, Kenton O'hara, Richard Harry Robert Harper, Eric John Greveson, David Alexander Butler, Stephen E Hodges

Detecting the location of a keyboard on a desktop

07/24/14 - 20140205138 - Methods and systems for detecting the location of a keyboard on a desktop. The method includes receiving an image of the desktop with the keyboard situated thereon and analyzing the image of the desktop to identify an area of the image corresponding to the keyboard. In one example, the image
Inventors: Peter John Ansell, Jamie Daniel Joseph Shotton, Christopher Jozef O'prey

Part and state detection for gesture recognition

07/24/14 - 20140204013 - Part and state detection for gesture recognition is useful for human-computer interaction, computer gaming, and other applications where gestures are recognized in real time. In various embodiments a decision forest classifier is used to label image elements of an input image with both part and state labels where part labels
Inventors: Christopher Jozef O'prey, Jamie Daniel Joseph Shotton, Peter John Ansell

Foreground and background image segmentation

05/08/14 - 20140126821 - Foreground and background image segmentation is described. In an example, a seed region is selected in a foreground portion of an image, and a geodesic distance is calculated from each image element to the seed region. A subset of the image elements having a geodesic distance less than a threshold
Inventors: Antonio Criminisi, Jamie Daniel Joseph Shotton, Andrew Fitzgibbon, Toby Sharp, Matthew Darius Cook

Pose tracking pipeline

03/20/14 - 20140078141 - A method of tracking a subject includes receiving from a source a depth image of a scene including the subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that image the subject and deriving from
Inventors: Robert Matthew Craig, Tommer Leyvand, Craig Peeper, Momin M. Al-ghosien, Matt Bronder, Oliver Williams, Ryan M. Geiss, Jamie Daniel Joseph Shotton, Johnny Lee, Mark Finocchio

Semi-supervised random decision forests for machine learning

12/26/13 - 20130346346 - Semi-supervised random decision forests for machine learning are described, for example, for interactive image segmentation, medical image analysis, and many other applications. In examples, a random decision forest comprising a plurality of hierarchical data structures is trained using both unlabeled and labeled observations. In examples, a training objective is used
Inventors: Antonio Criminisi, Jamie Daniel Joseph Shotton

Density estimation and/or manifold learning

12/26/13 - 20130343619 - Density estimation and/or manifold learning are described, for example, for computer vision, medical image analysis, text document clustering. In various embodiments a density forest is trained using unlabeled data to estimate the data distribution. In embodiments the density forest comprises a plurality of random decision trees each accumulating portions of
Inventors: Antonio Criminisi, Jamie Daniel Joseph Shotton, Ender Konukoglu

Human body pose estimation

10/10/13 - 20130266182 - Techniques for human body pose estimation are disclosed herein. Depth map images from a depth camera may be processed to calculate a probability that each pixel of the depth map is associated with one or more segments or body parts of a body. Body parts may then be constructed of
Inventors: Jamie Daniel Joseph Shotton, Andrew William Fitzgibbon

Real-time camera tracking using depth maps

09/19/13 - 20130244782 - Real-time camera tracking using depth maps is described. In an embodiment depth map frames are captured by a mobile depth camera at over 20 frames per second and used to dynamically update in real-time a set of registration parameters which specify how the mobile depth camera has moved. In examples
Inventors: Richard Newcombe, Shahram Izadi, David Molyneaux, Otmar Hilliges, David Kim, Jamie Daniel Joseph Shotton, Pushmeet Kohli, Andrew Fitzgibbon, Stephen Edward Hodges, David Alexander Butler

Pose tracking pipeline

09/19/13 - 20130241833 - A method of tracking a target includes receiving from a source a depth image of a scene including the human subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that belong to the human subject
Inventors: Robert Matthew Craig, Tommer Leyvand, Craig Peeper, Momin M. Al-ghosien, Matt Bronder, Oliver Williams, Ryan M. Geiss, Jamie Daniel Joseph Shotton, Johnny Lee, Mark Finocchio

Using high-level attributes to guide image processing

06/20/13 - 20130156298 - Using high-level attributes to guide image processing is described. In an embodiment high-level attributes of images of people such as height, torso orientation, body shape, gender are used to guide processing of the images for various tasks including but not limited to joint position detection, body part classification, medical image
Inventors: Pushmeet Kohli, Jamie Daniel Joseph Shotton, Min Sun

Learning image processing tasks from scene reconstructions

06/20/13 - 20130156297 - Learning image processing tasks from scene reconstructions is described where the tasks may include but are not limited to: image de-noising, image in-painting, optical flow detection, interest point detection. In various embodiments training data is generated from a 2 or higher dimensional reconstruction of a scene and from empirical images
Inventors: Jamie Daniel Joseph Shotton, Pushmeet Kohli, Stefan Johannes Josef Holzer, Shahram Izadi, Carsten Curt Eckard Rother, Sebastian Nowozin, David Kim, David Molyneaux, Otmar Hilliges

Computing pose and/or shape of modifiable entities

05/23/13 - 20130129230 - Computing pose and/or shape of a modifiable entity is described. In various embodiments a model of an entity (such as a human hand, a golf player holding a golf club, an animal, a body organ) is fitted to an image depicting an example of the entity in a particular pose
Inventors: Jamie Daniel Joseph Shotton, Andrew William Fitzgibbon, Jonathan James Taylor, Matthew Darius Cook

Depth image compression

05/02/13 - 20130106994 - Depth image compression is described for example, to enable body-part centers of players of a game to be detected in real time from depth images or for other applications such as augmented reality, and human-computer interaction. In an embodiment, depth images which have associated body-part probabilities, are compressed using probability
Inventors: Toby Sharp, Jamie Daniel Joseph Shotton

Pose tracking pipeline

01/31/13 - 20130028476 - A method of tracking a target includes receiving from a source a depth image of a scene including the human subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that belong to the human subject
Inventors: Robert Matthew Craig, Tommer Leyvand, Craig Peeper, Momim M. Al-ghosien, Matt Bronder, Oliver Williams, Ryan M. Geiss, Jamie Daniel Joseph Shotton, Johnny Lee, Mark Finocchio

Generating computer models of 3d objects

12/06/12 - 20120306876 - Generating computer models of 3D objects is described. In one example, depth images of an object captured by a substantially static depth camera are used to generate the model, which is stored in a memory device in a three-dimensional volume. Portions of the depth image determined to relate to the
Inventors: Jamie Daniel Joseph Shotton, Shahram Izadi, Otmar Hilliges, David Kim, David Molyneaux, Pushmeet Kohli, Andrew Fitzgibbon, Stephen Edward Hodges

Gesture recognition techniques

12/06/12 - 20120306734 - In one or more implementations, a static geometry model is generated, from one or more images of a physical environment captured using a camera, using one or more static objects to model corresponding one or more objects in the physical environment. Interaction of a dynamic object with at least one
Inventors: David Kim, Otmar D. Hilliges, Shahram Izadi, Patrick L. Olivier, Jamie Daniel Joseph Shotton, Pushmeet Kohli, David G. Molyneaux, Stephen E. Hodges, Andrew W. Fitzgibbon

Automatic organ localization

10/25/12 - 20120269407 - Automatic organ localization is described. In an example, an organ in a medical image is localized using one or more trained regression trees. Each image element of the medical image is applied to the trained regression trees to compute probability distributions that relate to a distance from each image element
Inventors: Antonio Criminisi, Jamie Daniel Joseph Shotton, Duncan Paul Robertson, Sayan D. Pathak, Steven James White, Khan Mohammed Siddiqui

Predicting joint positions

09/20/12 - 20120239174 - Predicting joint positions is described, for example, to find joint positions of humans or animals (or parts thereof) in an image to control a computer game or for other applications. In an embodiment image elements of a depth image make joint position votes so that for example, an image element
Inventors: Jamie Daniel Joseph Shotton, Pushmeet Kohli, Ross Brook Girshick, Andrew Fitzgibbon, Antonio Criminisi

Gesture detection and recognition

09/06/12 - 20120225719 - A gesture detection and recognition technique is described. In one example, a sequence of data items relating to the motion of a gesturing user is received. A selected set of data items from the sequence are tested against pre-learned threshold values, to determine a probability of the sequence representing a
Inventors: Sebastian Nowozin, Pushmeet Kohli, Jamie Daniel Joseph Shotton

Image labeling with global parameters

08/30/12 - 20120219209 - Image labeling with global parameters is described. In an embodiment a pose estimation system executes automatic body part labeling. For example, the system may compute joint recognition or body part segmentation for a gaming application. In another example, the system may compute organ labels for a medical imaging application. In
Inventors: Jamie Daniel Joseph Shotton, Pushmeet Kohli, Andrew Blake, Inmar-ella Givoni

Image registration

08/16/12 - 20120207359 - Image registration is described. In an embodiment an image registration system executes automatic registration of images, for example medical images. In an example, semantic information is computed for each of the images to be registered comprising information about the types of objects in the images and the certainty of that

Real-time camera tracking using depth maps

08/02/12 - 20120196679 - Real-time camera tracking using depth maps is described. In an embodiment depth map frames are captured by a mobile depth camera at over 20 frames per second and used to dynamically update in real-time a set of registration parameters which specify how the mobile depth camera has moved. In examples

Moving object segmentation using depth images

08/02/12 - 20120195471 - Moving object segmentation using depth images is described. In an example, a moving object is segmented from the background of a depth image of a scene received from a mobile depth camera. A previous depth image of the scene is retrieved, and compared to the current depth image using an

Reducing interference between multiple infra-red depth cameras

08/02/12 - 20120194650 - Systems and methods for reducing interference between multiple infra-red depth cameras are described. In an embodiment, the system comprises multiple infra-red sources, each of which projects a structured light pattern into the environment. A controller is used to control the sources in order to reduce the interference caused by overlapping

Mobile camera localization using depth maps

08/02/12 - 20120194644 - Mobile camera localization using depth maps is described for robotics, immersive gaming, augmented reality and other applications. In an embodiment a mobile depth camera is tracked in an environment at the same time as a 3D model of the environment is formed using the sensed depth data. In an embodiment,

Using a three-dimensional environment model in gameplay

08/02/12 - 20120194517 - Use of a 3D environment model in gameplay is described. In an embodiment, a mobile depth camera is used to capture a series of depth images as it is moved around and a dense 3D model of the environment is generated from this series of depth images. This dense 3D

Three-dimensional environment reconstruction

08/02/12 - 20120194516 - Three-dimensional environment reconstruction is described. In an example, a 3D model of a real-world environment is generated in a 3D volume made up of voxels stored on a memory device. The model is built from data describing a camera location and orientation, and a depth image with pixels indicating a

Pose tracking pipeline

06/21/12 - 20120157207 - A method of tracking a target includes receiving from a source a depth image of a scene including the human subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that belong to the human subject

Foreground and background image segmentation

12/01/11 - 20110293180 - Foreground and background image segmentation is described. In an example, a seed region is selected in a foreground portion of an image, and a geodesic distance is calculated from each image element to the seed region. A subset of the image elements having a geodesic distance less than a threshold

Human body pose estimation

09/01/11 - 20110210915 - Techniques for human body pose estimation are disclosed herein. Images such as depth images, silhouette images, or volumetric images may be generated and pixels or voxels of the images may be identified. The techniques may process the pixels or voxels to determine a probability that each pixel or voxel is

Automatic identification of image features

08/04/11 - 20110188715 - Automatic identification of image features is described. In an embodiment, a device automatically identifies organs in a medical image using a decision forest formed of a plurality of distinct, trained decision trees. An image element from the image is applied to each of the trained decision trees to obtain a

Image processing using masked restricted boltzmann machines

02/10/11 - 20110033122 - Image processing using masked restricted Boltzmann machines is described. In an embodiment restricted Boltzmann machines based on beta distributions are described which are implemented in an image processing system. In an embodiment a plurality of fields of masked RBMs are connected in series. An image is input into a masked

Human body pose estimation

11/04/10 - 20100278384 - Techniques for human body pose estimation are disclosed herein. Depth map images from a depth camera may be processed to calculate a probability that each pixel of the depth map is associated with one or more segments or body parts of a body. Body parts may then be constructed of

Data processing using restricted boltzmann machines

09/09/10 - 20100228694 - Data processing using restricted Boltzmann machines is described, for example, to pre-process continuous data and provide binary outputs. In embodiments, restricted Boltzmann machines based on either Gaussian distributions or Beta distributions are described which are able to learn and model both the mean and variance of data. In some embodiments,

Pose tracking pipeline

08/05/10 - 20100197390 - A method of tracking a target includes receiving from a source an observed depth image of a scene including the target. Each pixel of the observed depth image is labeled as either a foreground pixel belonging to the target or a background pixel not belonging to the target. Each foreground


### Jamie Daniel Joseph Shotton patent invention listings

The bibliographic references displayed about Jamie Daniel Joseph Shotton's patents are for a recent sample of Jamie Daniel Joseph Shotton's publicly published patent applications. The inventor/author may have additional bibliographic citations listed at the USPTO.gov. FreshPatents.com is not associated or affiliated in any way with the author/inventor or the United States Patent/Trademark Office but is providing this non-comprehensive sample listing for educational and research purposes using public bibliographic data published and disseminated from the United States Patent/Trademark Office public datafeed. This information is also available for free on the USPTO.gov website. If Jamie Daniel Joseph Shotton filed recent patent applications under another name, spelling or location then those applications could be listed on an alternate page. If no bibliographic references are listed here, it is possible there are no recent filings or there is a technical issue with the listing--in that case, we recommend doing a search on the USPTO.gov website.

###



Sign up for the FreshPatents.com FREE Keyword Monitor and check for keyword phrases (ie. "RFID" , "wireless", "web development", "fuel cells" etc.)...You will be notified when new patent applications and inventions are published that match your keywords. Also you can save for later research public patent/invention documents using our FREE Organizer. It takes only 30 seconds to sign up or login.

Advertise on FreshPatents.com - Rates & Info

###

FreshPatents.com Support - Terms & Conditions