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公开(公告)号：US20170201825A1
公开(公告)日：2017-07-13
申请号：US15441793
申请日：2017-02-24
Applicant: Microsoft Technology Licensing, LLC
Inventor： Oliver Arthur Whyte , Ross Cutler , Avronil Bhattacharjee , Adarsh Prakash Murthy Kowdle , Adam Kirk , Stanley T. Birchfield , Cha Zhang
CPC classification number: H04R1/406 , G01S3/80 , G06T7/75 , G06T2207/30196 , H04M3/567 , H04M3/568 , H04M2203/509 , H04M2242/30 , H04N5/23219 , H04N5/23296 , H04N7/142 , H04N7/147 , H04N7/15 , H04R3/005 , H04R29/005 , H04R2430/20
Abstract: Various examples related to determining a location of an active participant are provided. In one example, image data of a room from an image capture device is received. First audio data from a first microphone array at the image capture device is received. Second audio data from a second microphone array spaced from the image capture device is received. Using a three dimensional model, a location of the second microphone array is determined. Using the first audio data, second audio data, location of the second microphone array, and an angular orientation of the second microphone array, an estimated location of the active participant is determined.
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Patent Agency Ranking
公开(公告)号：US20160245641A1
公开(公告)日：2016-08-25
申请号：US14626018
申请日：2015-02-19
Applicant: Microsoft Technology Licensing, LLC
Inventor： Adarsh Prakash Murthy Kowdle , Adam Garnet Kirk , Cristian Canton Ferrer , Oliver Whyte , Sing Bing Kang
CPC classification number: G01B11/026 , G06T7/521 , G06T7/593 , G06T7/74 , G06T2207/10048 , G06T2207/20021
Abstract: An active rangefinder system disclosed herein parameterizes a set of transformations predicting different possible appearances of a projection feature projected into a three-dimensional scene. A matching module matches an image of the projected projection feature with one of the transformations, and a depth estimation module estimates a distance to an object reflecting the projection feature based on the transformation identified by the matching module.
Abstract translation: 本文公开的主动测距系统参数化了一组预测投影到三维场景中的投影特征的不同可能出现的变换。 匹配模块将投影投影特征的图像与变换之一相匹配，并且深度估计模块基于由匹配模块识别的变换来估计反映投影特征的对象的距离。
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公开(公告)号：US20170236286A1
公开(公告)日：2017-08-17
申请号：US15071133
申请日：2016-03-15
Applicant: Microsoft Technology Licensing, LLC
Inventor： Sean Ryan Francesco Fanello , Christoph Rhemann , Adarsh Prakash Murthy Kowdle , Vladimir Tankovich , David KIM , Shahram Izadi
CPC classification number: G06K9/6282 , G06K9/627 , G06T7/0057 , G06T7/521 , G06T2207/10028 , G06T2207/20081
Abstract: Techniques for determining depth for a visual content item using machine-learning classifiers include obtaining a visual content item of a reference light pattern projected onto an object, and determining shifts in locations of pixels relative to other pixels representing the reference light pattern. Disparity, and thus depth, for pixels may be determined by executing one or more classifiers trained to identify disparity for pixels based on the shifts in locations of the pixels relative to other pixels of a visual content item depicting in the reference light pattern. Disparity for pixels may be determined using a visual content item of a reference light pattern projected onto an object without having to match pixels between two visual content items, such as a reference light pattern and a captured visual content item.
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公开(公告)号：US09621795B1
公开(公告)日：2017-04-11
申请号：US14991847
申请日：2016-01-08
Applicant: Microsoft Technology Licensing, LLC
Inventor： Oliver Arthur Whyte , Ross Cutler , Avronil Bhattacharjee , Adarsh Prakash Murthy Kowdle , Adam Kirk , Stanley T. Birchfield , Cha Zhang
CPC classification number: H04R1/406 , G01S3/80 , G06T7/75 , G06T2207/30196 , H04M3/567 , H04M3/568 , H04M2203/509 , H04M2242/30 , H04N5/23219 , H04N5/23296 , H04N7/142 , H04N7/147 , H04N7/15 , H04R3/005 , H04R29/005 , H04R2430/20
Abstract: Various examples related to determining a location of an active speaker are provided. In one example, image data of a room from an image capture device is received and a three dimensional model is generated. First audio data from a first microphone array at the image capture device is received. Second audio data from a second microphone array laterally spaced from the image capture device is received. Using the three dimensional model, a location of the second microphone array with respect to the image capture device is determined. Using the audio data and the location and angular orientation of the second microphone array, an estimated location of the active speaker is determined. Using the estimated location, a setting for the image capture device is determined and outputted to highlight the active speaker.
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公开(公告)号：US09980040B2
公开(公告)日：2018-05-22
申请号：US15441793
申请日：2017-02-24
Applicant: Microsoft Technology Licensing, LLC
Inventor： Oliver Arthur Whyte , Ross Cutler , Avronil Bhattacharjee , Adarsh Prakash Murthy Kowdle , Adam Kirk , Stanley T. Birchfield , Cha Zhang
CPC classification number: H04R1/406 , G01S3/80 , G06T7/75 , G06T2207/30196 , H04M3/567 , H04M3/568 , H04M2203/509 , H04M2242/30 , H04N5/23219 , H04N5/23296 , H04N7/142 , H04N7/147 , H04N7/15 , H04R3/005 , H04R29/005 , H04R2430/20
Abstract: Various examples related to determining a location of an active participant are provided. In one example, image data of a room from an image capture device is received. First audio data from a first microphone array at the image capture device is received. Second audio data from a second microphone array spaced from the image capture device is received. Using a three dimensional model, a location of the second microphone array is determined. Using the first audio data, second audio data, location of the second microphone array, and an angular orientation of the second microphone array, an estimated location of the active participant is determined.
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公开(公告)号：US09916524B2
公开(公告)日：2018-03-13
申请号：US15071133
申请日：2016-03-15
Applicant: Microsoft Technology Licensing, LLC
Inventor： Sean Ryan Francesco Fanello , Christoph Rhemann , Adarsh Prakash Murthy Kowdle , Vladimir Tankovich , David Kim , Shahram Izadi
CPC classification number: G06K9/6282 , G06K9/627 , G06T7/0057 , G06T7/521 , G06T2207/10028 , G06T2207/20081
Abstract: Techniques for determining depth for a visual content item using machine-learning classifiers include obtaining a visual content item of a reference light pattern projected onto an object, and determining shifts in locations of pixels relative to other pixels representing the reference light pattern. Disparity, and thus depth, for pixels may be determined by executing one or more classifiers trained to identify disparity for pixels based on the shifts in locations of the pixels relative to other pixels of a visual content item depicting in the reference light pattern. Disparity for pixels may be determined using a visual content item of a reference light pattern projected onto an object without having to match pixels between two visual content items, such as a reference light pattern and a captured visual content item.
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