Abstract: Respiration data is measured from point cloud data containing points associated with an upper body of a user. A device may receive the point cloud data via a wired or wireless connection or may generate it from one or more image sensors integrated into the device. Movement of points associated with the upper body, or within a region of interest, may be measured to estimate a breath signal. The estimated breath signal may be further processed to derive values representative of the breath signal. The breath signal may be then provided to a given application or location.

Abstract: A depth mapping apparatus includes a stereoscopic depth mapping module, including a first and second cameras configured to capture pairs of respective first and second images of a scene, and a range-sensing module, configured to measure respective ranges from the apparatus to multiple points in the scene. A controller processes a first pair of the first and second images of the scene to compute a first depth map of the scene and associates at least some of the points at which the range-sensing module measured respective ranges with corresponding pixels in the first depth map. The controller computes a correction function for the stereoscopic depth mapping module by comparing the respective ranges measured by the range-sensing module to respective depth coordinates of the corresponding pixels in the first depth map and applies the correction function in computing subsequent depth maps based on subsequent pairs of the first and second images.

Abstract: An electronic device is provided that includes a first camera with a first field of view configured to capture a first image, a second camera with a second field of view configured to capture a second image, and control circuitry configured to output stereoscopic content based on the first and second images. The control circuitry can include image signal processors configured to process the first and second images, additional processors for performing stereo rectification and image stabilization on the first and second images, and a compression block for combining images output from the additional processors.

Abstract: A tracking system, comprising: multiple beacons, each associated with a different cyclic equivalence class of code-word length n, and each configured to broadcast a bit-stream comprising a repeating code-word, where the code-word belongs to the associated cyclic equivalence class; and a mobile tracking unit, comprising: a sensor, and a processor, wherein the sensor is configured to simultaneously detects at least some of the bit streams, and provide each sensed bit stream in real-time to the processor, wherein for each bit-stream received by the processor from the sensor, the processor is configured to identify the beacon that broadcasted the bit-stream using the first n received bits.

Abstract: A system comprising: a camera configured to capture one or more images of a user's hand; and a computer configured to: receive the one or more captured images, apply a mapping function to the received one or more images, thereby yielding one or more coordinates associated with at least one feature of the user's hand, wherein the mapping function is derived from a set of labeled images that are produced by applying a machine learning algorithm to training data which comprises images of a trainer's hand, wherein the images are labeled with coordinates obtained from multiple magnetic sensors attached to the trainer's hand.

Abstract: A system comprising: a camera configured to capture one or more images of a user's hand; and a computer configured to: receive the one or more captured images, apply a mapping function to the received one or more images, thereby yielding one or more coordinates associated with at least one feature of the user's hand, wherein the mapping function is derived from a set of labeled images that are produced by applying a machine learning algorithm to training data which comprises images of a trainer's hand, wherein the images are labeled with coordinates obtained from multiple magnetic sensors attached to the trainer's hand.

Abstract: A system comprising: a camera configured to capture one or more images of a user's hand; and a computer configured to: receive the one or more captured images, apply a mapping function to the received one or more images, thereby yielding one or more coordinates associated with at least one feature of the user's hand, wherein the mapping function is derived from a set of labeled images that are produced by applying a machine learning algorithm to training data which comprises images of a trainer's hand, wherein the images are labeled with coordinates obtained from multiple magnetic sensors attached to the trainer's hand.

Abstract: A system comprising: a handheld mobile device comprising: a projector unit configured to project an image; a camera; and a prism configured to reflect the image projected by the projector such that the camera can capture the projected image.

Abstract: A method of processing a set of images of a scene is disclosed. The set is represented by a plurality of picture-elements defining an image plane, and the images are characterized by different light intensity spatial distributions but the same viewpoint relative to the scene. The method comprises: for each of at least some picture-elements, independently selecting a direction parallel to the image plane, constructing a weighted vector field based on the selected direction, and calculating a height of a surface in the scene perpendicular to the image plane based on the constructed weighted vector field. The method further comprises reconstructing a three-dimensional spatial representation of the surface based on the heights; and transmitting the spatial representation to a computer readable medium.

Abstract: A tracking system, comprising: multiple beacons, each associated with a different cyclic equivalence class of code-word length n, and each configured to broadcast a bit-stream comprising a repeating code-word, where the code-word belongs to the associated cyclic equivalence class; and a mobile tracking unit, comprising: a sensor, and a processor, wherein the sensor is configured to simultaneously detects at least some of the bit streams, and provide each sensed bit stream in real-time to the processor, wherein for each bit-stream received by the processor from the sensor, the processor is configured to identify the beacon that broadcasted the bit-stream using the first n received bits.

Abstract: A system comprising: a camera configured to capture one or more images of a user's hand; and a computer configured to: receive the one or more captured images, apply a mapping function to the received one or more images, thereby yielding one or more coordinates associated with at least one feature of the user's hand, wherein the mapping function is derived from a set of labeled images that are produced by applying a machine learning algorithm to training data which comprises images of a trainer's hand, wherein the images are labeled with coordinates obtained from multiple magnetic sensors attached to the trainer's hand.

Abstract: A method of processing a set of images of a scene is disclosed. The set is represented by a plurality of picture-elements defining an image plane, and the images are characterized by different light intensity spatial distributions but the same viewpoint relative to the scene. The method comprises: for each of at least some picture-elements, independently selecting a direction parallel to the image plane, constructing a weighted vector field based on the selected direction, and calculating a height of a surface in the scene perpendicular to the image plane based on the constructed weighted vector field. The method further comprises reconstructing a three-dimensional spatial representation of the surface based on the heights; and transmitting the spatial representation to a computer readable medium.

Abstract: A method training a randomized decision tree through multiple iterations, each is based on: a) Receiving multiple data samples that include data subsets, each data subset corresponds to an attribute. b) Distributing the data subsets to slave processing units after sorting the data samples in consecutive ascending order by updating a first index that identifies trajectories of the training data samples through the tree nodes of the previous tree level. c) Simultaneously processing the data subsets to identify split functions for each tree node with respect to each data subset and updating a second index that identifies the trajectories of the training data samples through the tree node of the current tree level. d) Collecting the split functions from the slave processing units and constructing the current tree level by selecting a preferred split function for each tree node of the current tree level.

Abstract: A system comprising: a handheld mobile device comprising: a projector unit configured to project an image; a camera; and a prism configured to reflect the image projected by the projector such that the camera can capture the projected image.

Abstract: A method training a randomized decision tree through multiple iterations, each is based on: a) Receiving multiple data samples that include data subsets, each data subset corresponds to an attribute. b) Distributing the data subsets to slave processing units after sorting the data samples in consecutive ascending order by updating a first index that identifies trajectories of the training data samples through the tree nodes of the previous tree level. c) Simultaneously processing the data subsets to identify split functions for each tree node with respect to each data subset and updating a second index that identifies the trajectories of the training data samples through the tree node of the current tree level. d) Collecting the split functions from the slave processing units and constructing the current tree level by selecting a preferred split function for each tree node of the current tree level.