Abstract: A method for generating a three-dimensional (3D) model of an object includes: capturing images of the object from a plurality of viewpoints, the images including color images; generating a 3D model of the object from the images, the 3D model including a plurality of planar patches; for each patch of the planar patches: mapping image regions of the images to the patch, each image region including at least one color vector; and computing, for each patch, at least one minimal color vector among the color vectors of the image regions mapped to the patch; generating a diffuse component of a bidirectional reflectance distribution function (BRDF) for each patch of planar patches of the 3D model in accordance with the at least one minimal color vector computed for each patch; and outputting the 3D model with the BRDF for each patch.

Abstract: A hearing aid system presents a hearing impaired user with customized enhanced intelligibility sound in a preferred language. The system includes a model trained with a set of source speech data representing sampling from a speech population relevant to the user. The model is also trained with a set of corresponding alternative articulation of source data, pre-defined or algorithmically constructed during an interactive session with the user. The model creates a set of selected target speech training data from the set of alternative articulation data that is preferred by the user as being satisfactorily intelligible and clear. The system includes a machine learning model, trained to shift incoming source speech data to a preferred variant of the target data that the hearing aid system presents to the user.

Abstract: A hearing aid system presents a hearing impaired user with customized enhanced intelligibility speech sound in a preferred language while maintaining the voice identity of speaker. The system includes a neural network model trained with a set of source speech data representing sampling from a speech population relevant to the user. The model is also custom trained with a set of parallel or non-parallel alternative articulations, collected during an interactive session with user or algorithmically generated based on the hearing profile of the user or category of users with common linguistic and hearing profiles.

Abstract: A method for generating a three-dimensional (3D) model of an object includes: capturing images of the object from a plurality of viewpoints, the images including color images; generating a 3D model of the object from the images, the 3D model including a plurality of planar patches; for each patch of the planar patches: mapping image regions of the images to the patch, each image region including at least one color vector; and computing, for each patch, at least one minimal color vector among the color vectors of the image regions mapped to the patch; generating a diffuse component of a bidirectional reflectance distribution function (BRDF) for each patch of planar patches of the 3D model in accordance with the at least one minimal color vector computed for each patch; and outputting the 3D model with the BRDF for each patch.

Abstract: A method for generating a three-dimensional (3D) model of an object includes: capturing images of the object from a plurality of viewpoints, the images including color images; generating a 3D model of the object from the images, the 3D model including a plurality of planar patches; for each patch of the planar patches: mapping image regions of the images to the patch, each image region including at least one color vector; and computing, for each patch, at least one minimal color vector among the color vectors of the image regions mapped to the patch; generating a diffuse component of a bidirectional reflectance distribution function (BRDF) for each patch of planar patches of the 3D model in accordance with the at least one minimal color vector computed for each patch; and outputting the 3D model with the BRDF for each patch.

Abstract: A hearing aid system presents a hearing impaired user with customized enhanced intelligibility sound in a preferred language. The system includes a model trained with a set of source speech data representing sampling from a speech population relevant to the user. The model is also trained with a set of corresponding alternative articulation of source data, pre-defined or algorithmically constructed during an interactive session with the user. The model creates a set of selected target speech training data from the set of alternative articulation data that is preferred by the user as being satisfactorily intelligible and clear. The system includes a machine learning model, trained to shift incoming source speech data to a preferred variant of the target data that the hearing aid system presents to the user.

Abstract: A method for generating a three-dimensional model of an object, by a scanning system including a client-side device including: an acquisition system configured to capture images; and an interaction system including a display device and a network interface includes: capturing a plurality of images of the object by the acquisition system, the images being captured from a plurality of different poses of the acquisition system; computing depth maps from the images of the objects, each of the depth maps corresponding to one of the poses of the acquisition system; combining the depth maps to generate a combined point cloud; and displaying, on the display device, the combined point cloud or a 3D mesh model generated from the combined point cloud.

Abstract: A method for identifying and tracking objects includes: capturing one or more 3-D models of one or more objects in a scene using a three-dimensional (3-D) scanning system, the one or more 3-D models including color and geometry information of the one or more objects; and computing, by an analysis agent, one or more descriptors of the one or more 3-D models, each descriptor corresponding to a fixed-length feature vector; and retrieving metadata identifying the one or more objects based on the one or more descriptors.

Abstract: According to one embodiment of the present invention, a method for automatically generating metadata for a media document includes: computing a feature vector of the media document using a convolutional neural network; searching a collection of media documents for one or more matching media documents having corresponding feature vectors similar to the feature vector of the media document, each media document of the collection of media documents being associated with metadata; generating metadata for the media document based on the metadata associated with the one or more matching media documents; and displaying the media document in association with the generated metadata.

Abstract: A method for capturing a depth map includes: controlling a plurality of cameras to capture, concurrently, a plurality of first images during a first exposure interval, each of the cameras concurrently capturing a corresponding one of the first images, the cameras having overlapping fields of view; controlling a projection source to emit light at a first illumination level during the first exposure interval; controlling the cameras to capture, concurrently, a plurality of second images during a second exposure interval, each of the cameras concurrently capturing a corresponding one of the second images; controlling the projection source to emit light at a second illumination level during the second exposure interval, the second illumination level being different from the first illumination level; combining the first images with the second images to generate a depth map; and outputting the depth map.

Abstract: According to one embodiment of the present invention, a method for automatically generating metadata for a media document includes: computing a feature vector of the media document using a convolutional neural network; searching a collection of media documents for one or more matching media documents having corresponding feature vectors similar to the feature vector of the media document, each media document of the collection of media documents being associated with metadata; generating metadata for the media document based on the metadata associated with the one or more matching media documents; and displaying the media document in association with the generated metadata.

Abstract: According to one embodiment of the present invention, a method for automatically generating metadata for a media document includes: computing a feature vector of the media document using a convolutional neural network; searching a collection of media documents for one or more matching media documents having corresponding feature vectors similar to the feature vector of the media document, each media document of the collection of media documents being associated with metadata; generating metadata for the media document based on the metadata associated with the one or more matching media documents; and displaying the media document in association with the generated metadata.

Abstract: A method for identifying and tracking objects includes: capturing one or more 3-D models of one or more objects in a scene using a three-dimensional (3-D) scanning system, the one or more 3-D models including color and geometry information of the one or more objects; and computing, by an analysis agent, one or more descriptors of the one or more 3-D models, each descriptor corresponding to a fixed-length feature vector; and retrieving metadata identifying the one or more objects based on the one or more descriptors.

Abstract: A method for generating a three-dimensional model of an object, by a scanning system including a client-side device including: an acquisition system configured to capture images; and an interaction system including a display device and a network interface includes: capturing a plurality of images of the object by the acquisition system, the images being captured from a plurality of different poses of the acquisition system; computing depth maps from the images of the objects, each of the depth maps corresponding to one of the poses of the acquisition system; combining the depth maps to generate a combined point cloud; and displaying, on the display device, the combined point cloud or a 3D mesh model generated from the combined point cloud.

Abstract: Acquired three-dimensional positional information is used to identify user created gesture(s), which gesture(s) are classified to determine appropriate input(s) to an associated electronic device or devices. Preferably at at least one instance of a time interval, the posture of a portion of a user is recognized, based at least one factor such as shape, position, orientation, velocity. Posture over each of the instance(s) is recognized as a combined gesture. Because acquired information is three-dimensional, two gestures may occur simultaneously.

Abstract: A method for generating a three-dimensional model of an object, by a scanning system including a client-side device including: an acquisition system configured to capture images; and an interaction system including a display device and a network interface includes: capturing a plurality of images of the object by the acquisition system, the images being captured from a plurality of different poses of the acquisition system; computing depth maps from the images of the objects, each of the depth maps corresponding to one of the poses of the acquisition system; combining the depth maps to generate a combined point cloud; and displaying, on the display device, the combined point cloud or a 3D mesh model generated from the combined point cloud.

Abstract: A method for generating a three-dimensional (3D) model of an object includes: capturing images of the object from a plurality of viewpoints, the images including color images; generating a 3D model of the object from the images, the 3D model including a plurality of planar patches; for each patch of the planar patches: mapping image regions of the images to the patch, each image region including at least one color vector; and computing, for each patch, at least one minimal color vector among the color vectors of the image regions mapped to the patch; generating a diffuse component of a bidirectional reflectance distribution function (BRDF) for each patch of planar patches of the 3D model in accordance with the at least one minimal color vector computed for each patch; and outputting the 3D model with the BRDF for each patch.

Abstract: A method for generating a three-dimensional (3D) model of an object includes: capturing images of the object from a plurality of viewpoints, the images including color images; generating a 3D model of the object from the images, the 3D model including a plurality of planar patches; for each patch of the planar patches: mapping image regions of the images to the patch, each image region including at least one color vector; and computing, for each patch, at least one minimal color vector among the color vectors of the image regions mapped to the patch; generating a diffuse component of a bidirectional reflectance distribution function (BRDF) for each patch of planar patches of the 3D model in accordance with the at least one minimal color vector computed for each patch; and outputting the 3D model with the BRDF for each patch.

Abstract: A handheld three-dimensional scanning sensor including: a depth camera; a client communication interface; a controller; and a memory having instructions stored thereon that, when executed by the controller, cause the controller to: control the depth camera to capture an image; and transmit the image over the client communication interface.

Abstract: A method for generating a three-dimensional model of an object, by a scanning system including a client-side device including: an acquisition system configured to capture images; and an interaction system including a display device and a network interface includes: capturing a plurality of images of the object by the acquisition system, the images being captured from a plurality of different poses of the acquisition system; computing depth maps from the images of the objects, each of the depth maps corresponding to one of the poses of the acquisition system; combining the depth maps to generate a combined point cloud; and displaying, on the display device, the combined point cloud or a 3D mesh model generated from the combined point cloud.