Patents by Inventor Matthew Darius Cook
Matthew Darius Cook has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 9280719Abstract: 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 is determined, and this subset of image elements are labeled as foreground. In another example, an image element from an image showing at least a user, a foreground object in proximity to the user, and a background is applied to trained decision trees to obtain probabilities of the image element representing one of these items, and a corresponding classification assigned to the image element. This is repeated for each image element. Image elements classified as belonging to the user are labeled as foreground, and image elements classified as foreground objects or background are labeled as background.Type: GrantFiled: January 6, 2014Date of Patent: March 8, 2016Assignee: Microsoft Technology Licensing, LLCInventors: Antonio Criminisi, Jamie Daniel Joseph Shotton, Andrew Fitzgibbon, Toby Sharp, Matthew Darius Cook
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Patent number: 8724906Abstract: 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 and shape. In examples, an optimization process finds values of pose and/or shape parameters that when applied to the model explain the image data well. In examples the optimization process is influenced by correspondences between image elements and model points obtained from a regression engine where the regression engine may be a random decision forest. For example, the random decision forest may take elements of the image and calculate candidate correspondences between those image elements and model points. In examples the model, pose and correspondences may be used for control of various applications including computer games, medical systems, augmented reality.Type: GrantFiled: November 18, 2011Date of Patent: May 13, 2014Assignee: Microsoft CorporationInventors: Jamie Daniel Joseph Shotton, Andrew William Fitzgibbon, Jonathan James Taylor, Matthew Darius Cook
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Publication number: 20140126821Abstract: 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 is determined, and this subset of image elements are labeled as foreground. In another example, an image element from an image showing at least a user, a foreground object in proximity to the user, and a background is applied to trained decision trees to obtain probabilities of the image element representing one of these items, and a corresponding classification assigned to the image element. This is repeated for each image element. Image elements classified as belonging to the user are labeled as foreground, and image elements classified as foreground objects or background are labeled as background.Type: ApplicationFiled: January 6, 2014Publication date: May 8, 2014Applicant: Microsoft CorporationInventors: Antonio Criminisi, Jamie Daniel Joseph Shotton, Andrew Fitzgibbon, Toby Sharp, Matthew Darius Cook
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Patent number: 8638985Abstract: 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 associated with a segment of a body captured in the image or to determine a three-dimensional representation for each pixel or voxel that is associated with a location on a canonical body. These probabilities or three-dimensional representations may then be utilized along with the images to construct a posed model of the body captured in the image.Type: GrantFiled: March 3, 2011Date of Patent: January 28, 2014Assignee: Microsoft CorporationInventors: Jamie Daniel Joseph Shotton, Shahram Izadi, Otmar Hilliges, David Kim, David Geoffrey Molyneaux, Matthew Darius Cook, Pushmeet Kohli, Antonio Criminisi, Ross Brook Girshick, Andrew William Fitzgibbon
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Patent number: 8625897Abstract: 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 is determined, and this subset of image elements are labeled as foreground. In another example, an image element from an image showing at least a user, a foreground object in proximity to the user, and a background is applied to trained decision trees to obtain probabilities of the image element representing one of these items, and a corresponding classification assigned to the image element. This is repeated for each image element. Image elements classified as belonging to the user are labeled as foreground, and image elements classified as foreground objects or background are labeled as background.Type: GrantFiled: May 28, 2010Date of Patent: January 7, 2014Assignee: Microsoft CorporationInventors: Antonio Criminisi, Jamie Daniel Joseph Shotton, Andrew Fitzgibbon, Toby Sharp, Matthew Darius Cook
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Publication number: 20130129230Abstract: 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 and shape. In examples, an optimization process finds values of pose and/or shape parameters that when applied to the model explain the image data well. In examples the optimization process is influenced by correspondences between image elements and model points obtained from a regression engine where the regression engine may be a random decision forest. For example, the random decision forest may take elements of the image and calculate candidate correspondences between those image elements and model points. In examples the model, pose and correspondences may be used for control of various applications including computer games, medical systems, augmented reality.Type: ApplicationFiled: November 18, 2011Publication date: May 23, 2013Applicant: MICROSOFT CORPORATIONInventors: Jamie Daniel Joseph Shotton, Andrew William Fitzgibbon, Jonathan James Taylor, Matthew Darius Cook
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Publication number: 20110293180Abstract: 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 is determined, and this subset of image elements are labeled as foreground. In another example, an image element from an image showing at least a user, a foreground object in proximity to the user, and a background is applied to trained decision trees to obtain probabilities of the image element representing one of these items, and a corresponding classification assigned to the image element. This is repeated for each image element. Image elements classified as belonging to the user are labeled as foreground, and image elements classified as foreground objects or background are labeled as background.Type: ApplicationFiled: May 28, 2010Publication date: December 1, 2011Applicant: Microsoft CorporationInventors: Antonio Criminisi, Jamie Daniel Joseph Shotton, Andrew Fitzgibbon, Toby Sharp, Matthew Darius Cook
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Publication number: 20110210915Abstract: 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 associated with a segment of a body captured in the image or to determine a three-dimensional representation for each pixel or voxel that is associated with a location on a canonical body. These probabilities or three-dimensional representations may then be utilized along with the images to construct a posed model of the body captured in the image.Type: ApplicationFiled: March 3, 2011Publication date: September 1, 2011Applicant: MICROSOFT CORPORATIONInventors: Jamie Daniel Joseph Shotton, Shahram Izadi, Otmar Hilliges, David Kim, David Geoffrey Molyneaux, Matthew Darius Cook, Pushmeet Kohli, Antonio Criminisi, Ross Brook Girshick, Andrew William Fitzgibbon