公开(公告)号：US09542749B2

公开(公告)日：2017-01-10

申请号：US14148184

申请日：2014-01-06

Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor： Daniel Freedman ,  Eyal Krupka ,  Yoni Smolin ,  Ido Leichter ,  Mirko Schmidt

IPC: G06K9/00 ,  G06T7/00 ,  H04N13/00 ,  H04N13/02 ,  G01S17/36 ,  G01S17/89 ,  G01S7/497

CPC classification number: G06T7/593 ,  G01S7/497 ,  G01S17/36 ,  G01S17/89 ,  G06T7/536 ,  G06T2207/10028 ,  G06T2207/20076 ,  H04N13/128 ,  H04N13/271

Abstract: Fast general multipath correction in time of flight imaging is described, for example, to obtain accurate depth maps at frame rate from a time of flight camera. In various embodiments accurate depth maps are calculated by looking up corrected depth values stored in a look up table. In various embodiments the corrected depth values are highly accurate as they take into account three or more possible light ray paths between the camera and a surface in a scene being imaged. In an example accurate depth maps are computed at a frame rate of a time of flight camera. In an example accurate depth maps are computed in less than 30 milliseconds for an image having over 200,000 pixels using a standard CPU.

Abstract translation: 描述飞行时间成像时的快速一般多路径校正，例如，从飞行时间相机的帧速率获得准确的深度图。 在各种实施例中，通过查找存储在查找表中的校正深度值来计算精确的深度图。 在各种实施例中，校正的深度值是高度准确的，因为它们考虑了相机和被成像的场景中的表面之间的三个或更多个可能的光线路径。 在一个示例中，以飞行时间相机的帧速率计算精确的深度图。 在一个示例中，使用标准CPU对于具有超过200,000个像素的图像，在小于30毫秒内计算精确的深度图。

公开(公告)号：US11688399B2

公开(公告)日：2023-06-27

申请号：US17115293

申请日：2020-12-08

Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor： Adi Diamant ,  Karen Master Ben-Dor ,  Eyal Krupka ,  Raz Halaly ,  Yoni Smolin ,  Ilya Gurvich ,  Aviv Hurvitz ,  Lijuan Qin ,  Wei Xiong ,  Shixiong Zhang ,  Lingfeng Wu ,  Xiong Xiao ,  Ido Leichter ,  Moshe David ,  Xuedong Huang ,  Amit Kumar Agarwal

IPC: H04N7/14 ,  G10L15/26 ,  H04N7/15 ,  G10L17/00 ,  G06V40/16

CPC classification number: G10L15/26 ,  G06V40/172 ,  G10L17/00 ,  H04N7/15

Abstract: A method for facilitating a remote conference includes receiving a digital video and a computer-readable audio signal. A face recognition machine is operated to recognize a face of a first conference participant in the digital video, and a speech recognition machine is operated to translate the computer-readable audio signal into a first text. An attribution machine attributes the text to the first conference participant. A second computer-readable audio signal is processed similarly, to obtain a second text attributed to a second conference participant. A transcription machine automatically creates a transcript including the first text attributed to the first conference participant and the second text attributed to the second conference participant.