Abstract: Techniques for generating a gallery view of tiles for in-area participants who are participating in an online meeting are disclosed. A video stream is accessed, where this stream includes an area view of an area in which an in-area participant is located. This area view comprises pixels representative of the area and pixels representative of the in-area participant. The pixels representative of the in-area participant are identified. A field of view of the in-area participant is generated. A tile of the in-area participant is generated based on the field of view. This tile is then displayed while the area view is not displayed.

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.

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.

Abstract: Systems and methods are provided for configuring and utilizing botcasts, which comprise audio content with transitions corresponding to the audio content, to facilitate accessibility and presentation of the media content within the botcasts according to contextual relevance for different individual users. The systems identify, access, filter, augment, customize, personalize, create and/or otherwise configure the media content, as well as the content transitions in the botcasts, according to the individual preferences and profiles of each user, as well as the contextual circumstances for each user.

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.

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.

Abstract: A gesture recognition method comprises receiving at a processor from a sensor a sequence of captured signal frames for extracting hand pose information for a hand and using at least one trained predictor executed on the processor to extract hand pose information from the received signal frames. For at least one defined gesture, defined as a time sequence comprising hand poses, with each of the hand poses defined as a conjunction or disjunction of qualitative propositions relating to interest points on the hand, truth values are computed for the qualitative propositions using the hand pose information extracted from the received signal frames, and execution of the gesture is tracked, by using the truth values to determine which of the hand poses in the time sequence have already been executed and which of the hand poses in the time sequence is expected next.

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.

Abstract: A gesture recognition method comprises receiving at a processor from a sensor a sequence of captured signal frames for extracting hand pose information for a hand and using at least one trained predictor executed on the processor to extract hand pose information from the received signal frames. For at least one defined gesture, defined as a time sequence comprising hand poses, with each of the hand poses defined as a conjunction or disjunction of qualitative propositions relating to interest points on the hand, truth values are computed for the qualitative propositions using the hand pose information extracted from the received signal frames, and execution of the gesture is tracked, by using the truth values to determine which of the hand poses in the time sequence have already been executed and which of the hand poses in the time sequence is expected next.

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: 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.