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公开(公告)号：US10761612B2
公开(公告)日：2020-09-01
申请号：US16413515
申请日：2019-05-15
Applicant: Microsoft Technology Licensing, LLC
Inventor： 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
Abstract: 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 of the static objects is identified by analyzing at least one image and a gesture is recognized from the identified interaction of the dynamic object with the at least one of the static objects to initiate an operation of the computing device.
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公开(公告)号：US10331222B2
公开(公告)日：2019-06-25
申请号：US15162905
申请日：2016-05-24
Applicant: Microsoft Technology Licensing, LLC
Inventor： 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
IPC: G06F3/01 , G06K9/00 , G06F3/03 , G06F3/0481 , G06T7/73
Abstract: 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 of the static objects is identified by analyzing at least one image and a gesture is recognized from the identified interaction of the dynamic object with the at least one of the static objects to initiate an operation of the computing device.
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公开(公告)号：US10311282B2
公开(公告)日：2019-06-04
申请号：US15701170
申请日：2017-09-11
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jamie Daniel Joseph Shotton , Cem Keskin , Christoph Rhemann , Toby Sharp , Duncan Paul Robertson , Pushmeet Kohli , Andrew William Fitzgibbon , Shahram Izadi
IPC: G06T7/11 , G01S17/36 , G06K9/00 , G01S17/89 , G06K9/62 , G01S17/10 , G01S7/48 , G01S7/491 , G06T7/50
Abstract: 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 camera (such as human hands, bodies or any other objects). The raw image is input to a trained region detector and in response one or more regions of interest in the raw image are received. A received region of interest comprises image elements of the raw image which are predicted to depict at least part of one of the objects. A depth computation logic computes depth from the one or more regions of interest of the raw image.
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公开(公告)号：US20180275967A1
公开(公告)日：2018-09-27
申请号：US15470784
申请日：2017-03-27
Applicant: Microsoft Technology Licensing, LLC
Inventor： Abdelrahman S.A. Mohamed , Rishabh Singh , Lihong Li , Dengyong Zhou , Pushmeet Kohli , Emilio Parisotto
CPC classification number: G06F8/30 , G06N3/0445 , G06N3/0454 , G06N3/08 , G06N3/105
Abstract: Described are systems, methods, and computer-readable media for program generation in a domain-specific language based on input-output examples. In accordance with various embodiments, a neural-network-based program generation model conditioned on an encoded set of input-output examples is used to generate a program tree by iteratively expanding a partial program tree, beginning with a root node and ending when all leaf nodes are terminal.
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公开(公告)号：US20170316347A1
公开(公告)日：2017-11-02
申请号：US15652140
申请日：2017-07-17
Applicant: Microsoft Technology Licensing, LLC
Inventor： Matteo Venanzi , John Philip Guiver , Gabriella Kazai , Pushmeet Kohli , Milad Shokouhi
CPC classification number: G06N20/00 , G06N5/043 , G06N7/005 , G06Q10/06311 , H04L51/12
Abstract: Crowdsourcing systems with machine learning are described. Specifically, item-label inference methods and systems are presented, for example, to provide aggregated answers to a crowdsourced task in a manner achieving good accuracy even where observed data about past behavior of crowd members is sparse. In various examples, an item-label inference system infers variables describing characteristics of both individual crowd workers and communities of the workers. In various examples, an item-label inference system provides aggregated labels while considering the inferred worker characteristics and the inferred characteristics of the worker communities. In examples the item-label inference system provides uncertainty information associated with the inference results for selecting workers and generating future tasks.
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Patent Agency Ranking
公开(公告)号：US20160370867A1
公开(公告)日：2016-12-22
申请号：US15162905
申请日：2016-05-24
Applicant: Microsoft Technology Licensing, LLC
Inventor： 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
CPC classification number: G06F3/017 , G06F3/011 , G06F3/0304 , G06F3/04815 , G06K9/00355 , G06T7/75 , G06T2207/10028 , G06T2207/30196
Abstract: 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 of the static objects is identified by analyzing at least one image and a gesture is recognized from the identified interaction of the dynamic object with the at least one of the static objects to initiate an operation of the computing device.
Abstract translation: 在一个或多个实现中，使用一个或多个静态对象来对物理环境中的一个或多个对象进行建模，从使用照相机捕获的物理环境的一个或多个图像生成静态几何模型。 通过分析至少一个图像来识别动态对象与静态对象中的至少一个的交互，并且从所识别的动态对象与所述静态对象中的至少一个的交互中识别手势以发起计算的操作 设备。
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公开(公告)号：US20160104031A1
公开(公告)日：2016-04-14
申请号：US14513746
申请日：2014-10-14
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jamie Daniel Joseph Shotton , Cem Keskin , Christoph Rhemann , Toby Sharp , Duncan Paul Robertson , Pushmeet Kohli , Andrew William Fitzgibbon , Shahram Izadi
CPC classification number: G06K9/00201 , G01S7/4808 , G01S7/4911 , G01S17/10 , G01S17/36 , G01S17/89 , G06K9/00362 , G06K9/00671 , G06K9/6282 , G06T7/11 , G06T7/50 , G06T2207/10028 , G06T2207/10048 , G06T2207/10152 , G06T2207/20081
Abstract: 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 camera (such as human hands, bodies or any other objects). The raw image is input to a trained region detector and in response one or more regions of interest in the raw image are received. A received region of interest comprises image elements of the raw image which are predicted to depict at least part of one of the objects. A depth computation logic computes depth from the one or more regions of interest of the raw image.
Abstract translation: 描述飞行时间图像中的感兴趣区域检测。 例如，计算设备在飞行时间相机接收针对单个帧捕获的至少一个原始图像。 原始图像描绘了飞行时间相机（例如人的手，身体或任何其他物体）的环境中的一个或多个物体。 将原始图像输入到经过训练的区域检测器，并且作为响应，接收原始图像中的一个或多个感兴趣区域。 接收的感兴趣区域包括被预测为描绘其中一个对象的至少一部分的原始图像的图像元素。 深度计算逻辑从原始图像的一个或多个感兴趣区域计算深度。
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公开(公告)号：US11120373B2
公开(公告)日：2021-09-14
申请号：US14448628
申请日：2014-07-31
Applicant: Microsoft Technology Licensing, LLC
Inventor： Matteo Venanzi , John Philip Guiver , Pushmeet Kohli
IPC: G06Q10/06
Abstract: Crowdsourcing using active learning is described, for example, to select pairs of tasks and groups of workers so that information gained about answers to the tasks in the pool is optimized. In various examples a machine learning system learns variables describing characteristics of communities of workers, characteristics of workers, task variables and uncertainty of these variables. In various examples, the machine learning system predicts task variables and uncertainty of the predicted task variables for possible combinations of communities of workers and tasks. In examples the predicted variables and uncertainty are used to calculate expected information gain of the possible combinations and to rank the possible combinations. In examples, the crowdsourcing system uses the expected information gain to allocate tasks to worker communities and observe the results; the results may then be used to update the machine learning system.
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公开(公告)号：US10127497B2
公开(公告)日：2018-11-13
申请号：US14514162
申请日：2014-10-14
Applicant: Microsoft Technology Licensing, LLC
Inventor： Seyed Mohammadali Eslami , Daniel Stefan Tarlow , Pushmeet Kohli , John Winn
Abstract: An inference engine is described for efficient machine learning. For example, an inference engine executes a plurality of ordered steps to carry out inference on the basis of observed data. For each step, a plurality of inputs to the step are received. A predictor predicts an output of the step and computes uncertainty of the prediction. Either the predicted output or a known output is selected on the basis of the uncertainty. If the known output is selected, the known output is computed, (for example, using a resource intensive, accurate process). The predictor is retrained using the known output and the plurality of inputs of the step as training data. For example, computing the prediction is fast and efficient as compared with computing the known output.
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公开(公告)号：US10110881B2
公开(公告)日：2018-10-23
申请号：US14528928
申请日：2014-10-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jamie Daniel Joseph Shotton , Toby Sharp , Jonathan James Taylor , Pushmeet Kohli , Shahram Izadi , Andrew William Fitzgibbon , Reinhard Sebastian Bernhard Nowozin
IPC: H04N13/02 , H04N13/279 , G06K9/62 , G06K9/00
Abstract: 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 and used to render, from the 3D model, simulations of raw time of flight image data depicting the entity in a specified pose/shape. The simulated raw image data and at least part of the received raw image data are compared and on the basis of the comparison, parameters of the entity are computed.
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