Abstract: Devices, methods, and non-transitory program storage devices (NPSDs) are disclosed herein to provide for the privacy-respectful learning of iconic scenes and places, wherein the learning is based on information received from one or more client devices in response to one or more collection criteria specified as part of one or more collection operations launched by a server device. In some embodiments, differential privacy techniques (such as the submission of predetermined amounts of noise-injecting, e.g., randomly-generated, data in conjunction with actual data) are employed by the client devices, such that any insights learned by the server device only relate to “hot spots,” “themes,” or other scenes, objects, and/or topics that are highly popular and captured in the digital assets (DAs) of many users, ensuring there is no way for the server device to learn or glean any insights related to particular users of individual client devices participating in the collection operations.

Abstract: Techniques for digital asset classification are described. One or more holiday score are assigned to a set of digital media assets. The one or more holiday scores are calculated based on weighted holiday metrics determined based on characteristics of the set of digital assets. A holiday classification is assigned based on the one or more holiday score, and the set of digital media assets are provided for presentation in accordance with the holiday classification.

Abstract: Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to receive, at a client device, a machine learning model from a server, detect a usage pattern for a content item, store an association between the content item and the detected usage pattern in local data, train the machine learning model using local data for the content item with the detected usage pattern to generate a trained machine learning model, generate an update for the machine learning model, privatize the update for the machine learning model, and transmit the privatized update for the machine learning model to the server.

Abstract: Detection of human hands within an environment is used for gesture recognition for input to a computing system. Detecting a hand is non-trivial, however, because the appearances of a hand vary widely with many independent digits and moving parts. A feature-based similarity measurement is used to detect two specific hand positions—an open hand and a closed hand.

Abstract: A system may utilize sound localization techniques, such as time-difference-of-arrival techniques, to estimate an audio-based sound source position from which a sound originates. An optical image or depth map of an area containing the sound source location may then captured and analyzed to detect an object that is known or expected to have produced the sound. The position of the object may also be determined based on the analysis of the optical image or depth map. The position of the sound source may then be determined based at least in part on the position of the detected object or on a combination of the audio-based sound source position and the determined position of the object.

Abstract: In order to classify the presented pose of a human hand, a feature distribution map of the pose is compared to reference feature distribution maps. To generate a feature distribution map, each contour point of a hand is analyzed to determine a corresponding feature set. The feature set of a contour point includes a distance feature and an angle feature of the contour point in relation to one of its neighboring contour points. The feature sets generated from an observed pose are compared to feature sets of reference poses to determine which of the reference poses most closely matches the presented pose.

Abstract: A hand gesture may be characterized mathematically as a set of motion parameters applied to a dynamic motion model. Training may be conducted to compile a library of motion parameter sets corresponding to various gestures. Motion parameters corresponding to observed gestures may than be compared to the library of motion parameter sets to classify the observed gestures.

Abstract: Techniques are described for projecting content onto a display surface and for analyzing the hand of a user that is placed over the display surface, such as when the user is making a command gesture. Based on the location and known shape of the display surface, a surface depth map is calculated corresponding to the display surface. Points of the calculated surface depth map are then subtracted from respective points an observed depth map to produce a foreground depth map corresponding to the user hand. The hand may be further characterized and tracked based on the foreground depth map.

Abstract: An augmented reality environment allows interaction between virtual and real objects. Wavelength modulated light with a known spectral signature available within the augmented reality environment is used to generate spectral reflectance data for objects within the environment. This reflectance data is used to determine whether objects or portions thereof are skin.

Abstract: Techniques are described for identifying suitable surfaces within a room upon which visual content may be displayed. One or more images of the room are obtained and used to identify planar surfaces. The images may also be analyzed to determine the visual textures of the surfaces within the room. Suitable projection surfaces may then be selected from the identified planar surfaces, based on the visual textures of the planar surfaces.

Abstract: A system may utilize a projector, a camera, and a depth sensor to produce images within the environment of a user and to detect and respond to user actions. Depth data from the depth sensor may be analyzed to detect and identify items within the environment. A coordinate transformation may then be used to identify corresponding color values from camera data, which can in turn be analyzed to determine the colors of detected items. A similar coordinate transformation may be used to identify color values of a projected image that correspond to the detected items. In some cases, camera color values corresponding to an item may be corrected based on the corresponding color values of a projected image. In other cases, projected color values corresponding to an item may be corrected based on the corresponding camera color values.

Abstract: Described herein are techniques and systems to determine movement of an imaging device (egomotion) using an analysis of images captured the by imaging device. The imaging device, while in a first position, may capture a first image of an environment. The image may be a depth map, a still photograph, or other type of image that enables identification of objects, reference features, and/or other characteristics of the environment. The imaging device may then capture a second image from a second position within the environment after the imaging devices moves from the first position to the second position. A comparison of corresponding reference features from the first image and second image may be used to determine translation and rotation of the imaging device.

Abstract: Techniques are described for detecting a hand gesture made by a user. Fingertips of a hand may be identified and tracked over time. When a user contracts the fingertips from an extended position, hand spread may be calculated based on the area of the hand and fingers. The hand spread over time may be compared to a Gaussian function to evaluate whether the observed motion represents a grasping motion.

Abstract: A wireless transmit/receive unit (WTRU) and a dynamic spectrum management (DSM) engine are described. The WTRU includes a transceiver, a radio frequency (RF) spectrum sensing unit and a processing unit. The transceiver transmits over a wireless link. The RF spectrum sensing unit measures information indicative of usage of a spectrum by other devices. The processing unit detects a change in performance of the wireless link, controls the transceiver to transmit a notification to a DSM engine indicating that the change in the performance of the wireless link was detected on a condition that the processing unit detects the change in performance of the wireless link, and receives a sensing task request for the WTRU to measure the information indicative of the usage of the spectrum by other devices based on the notification transmitted to the DSM engine indicating that the change in the performance of the wireless link was detected.