Google Inc.

Thor Carpenter

Frank Losasso Petterson

Janahan Vivekanandan

Chee Heng Chew

Gregory Marshall Burgess

Christian Plagemann

Navneet Dalal

Antonio Bernardo Monteiro Costa

Rahul Garg

Mark Chang

Mehul Nariyawala

John David Salazar

Anil Christopher Kokaram

Timothy Samuel Psiaki

Greg Burgess

Tim Psiaki

Ryan Petrie

Gregory M Burgess

A processing device collects depth data for frames in a sequence of images of a video stream being provided by a source device to a target device as part of a communication session. The depth data is created by a depth aware camera of the source device. The processing device maps, using the depth data, feature locations of the features of an object in a frame to feature locations of the features of the object in other frames, determines overlapping frame sections between the frames using the mapped feature locations, modifies, in the sequence of images, a set of images corresponding to the frames based on the overlapping frame sections to create a stabilized stream of images for the video stream, and provides the stabilized stream of images in the video stream as part of the communication session.

Depth camera based image stabilization

Systems and methods are disclosed for gesture-initiated actions in videoconferences. In one implementation, a processing device receives content streams during a communication session, identifies a request for feedback within one of the content streams, based on an identification of the request for feedback, processes the content streams to identify one or more gestures within at least one of the content streams, and based on a determination that a first gesture of the one or more gestures is relatively more prevalent across the content streams than one or more other gestures, initiates an action with respect to the communication session.

Gesture-initiated actions in videoconferences

Systems and methods are disclosed for gesture-initiated actions in videoconferences. In one implementation, a processing device receives one or more content streams as part of a communication session. The processing device identifies, within the one or more content streams, a request for feedback. The processing device processes, based on an identification of a request for feedback within the one of the plurality of content streams, the one or more content streams to identify a presence of one or more gestures within at least one of the one or more content streams. The processing device initiates, based on an identification of the presence of one or more gestures within at least one of the one or more content streams, an action with respect to the communication session.

Implementations generally relate to enhancing content appearance. In some implementations, a method includes receiving an image, selecting a reference object in the image. The method also includes determining one or more image parameter adjustments based on the selected reference object, and applying the one or more image parameter adjustments to the entire image.

Enhancing content appearance

Embodiments generally relate to presenter control during a video conference. In one embodiment, a method includes providing a user interface of a video conference between at least a first user and a second user, where the user interface displays a first user video stream in a current presenter position and displays a second user video stream in a secondary presenter position. The method also includes detecting at least one gesture from the second user, where the detecting of the at least one gesture indicates that the second user intends to be a current presenter. The method also includes causing the user interface to display the second user video stream in the current presenter position and to display the first user video stream in the secondary presenter position in response to the detecting.

Presenter control during a video conference

Methods and systems are provided allowing for background identification and gesture recognition in video images. A computer-implemented image processing method includes: receiving, using at least one processing circuit, a plurality of image frames of a video; constructing, using at feast one processing circuit, a plurality of statistical models of the plurality of image frames at a plurality of pixel granularity levels; constructing, using at least one processing circuit, a plurality of probabilistic models of an input image frame at a plurality of channel granularity levels based on the plurality of statistical models; merging at least some of the plurality of probabilistic models based on a weighted average to form a single probability image; determining background pixels, based on a probability threshold value, from the single probability image; and determining whether the plurality of image frames, when examined in a particular sequence, conveys a gesture by the object.

Background detection as an optimization for gesture recognition

Implementations relate to blurring an image while preserving details in the image. In some implementations, a method includes receiving an input image and creating a blurred image from the input image by averaging pixel intensities of a plurality of pixels in the input image. The method creates a transparency mask from the input image, where the transparency mask indicates transparency values for pixel positions of the input image based on edges detected in the input image. The pixel intensities of the input image and pixel intensities of the blurred image are composited based on the transparency values of the transparency mask to create an output image having greater blur than the input image in areas outside the detected edges.

Image blur with preservation of detail

Embodiments generally relate to processing media streams during a multi-user video conference. In one embodiment, a method includes obtaining at least one frame from a media stream, and determining a plurality of coordinates within the at least one frame. The method also includes obtaining at least one media content item, obtaining one or more parameters from a remote user, and adding the at least one media content item to the at least one frame based on the plurality of coordinates and the one or more parameters.

Processing media streams during a multi-user video conference

Embodiments generally relate to processing media streams during a multi-user video conference. In one embodiment, a method includes obtaining at least one audio file and obtaining one or more parameters from a remote user. The method also includes adding user-specified audio content from the at least one audio file to a media stream based on the one or more parameters.

Implementations generally relate to modifying an appearance of a participant during a video conference. In some implementations, a method includes obtaining at least one frame from a media stream, where the at least one frame includes a first face, and where the first face is an image of a face associated with a user. The method also includes determining a plurality of coordinates within the at least one frame. The method also includes obtaining at least one media content item. The method also includes adding the at least one media content item to the at least one frame based on the plurality of coordinates.

Modifying an appearance of a participant during a video conference

A method and computing system for receiving an indication of a first user participating in a videoconference. A determination is made concerning whether bandwidth compression is preferable for the first user of the videoconference. If bandwidth compression is preferable for the first user of the videoconference: an unprocessed video stream associated with the first user is processed to generate a reduced bandwidth video stream, and the reduced bandwidth video stream is provided to a video encoder. Processing the unprocessed video stream includes performing an edge detection procedure on the unprocessed video stream.

Bandwidth reduction system and method

Methods and systems are provided allowing for background identification in video images. A computer-implemented image processing method includes: receiving, using at least one processing circuit, a plurality of image frames of a video; constructing, using at least one processing circuit, a plurality of statistical models of the plurality of image frames at a plurality of pixel granularity levels; constructing, using at least one processing circuit, a plurality of probabilistic models of an input image frame at a plurality of channel granularity levels based on the plurality of statistical models; merging at least some of the plurality of probabilistic models based on a weighted average to form a single probability image; and determining background pixels, based on a probability threshold value, from the single probability image.

Automatic background identification in video images

A computer-implemented image processing method includes: receiving, using at least one processing circuit, a plurality of image frames of a video; constructing, using at least one processing circuit, a plurality of statistical models of the plurality of image frames at a plurality of pixel granularity levels; constructing, using at least one processing circuit, a plurality of probabilistic models of an input image frame at a plurality of channel granularity levels based on the plurality of statistical models; merging at least some of the plurality of probabilistic models based on a weighted average to form a single probability image; and determining background pixels, based on a probability threshold value, from the single probability image.

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Snoqualmie, WA, United States of America

Thor Carpenter