A camera tracking system for computer assisted navigation during surgery operatively determines a first pose of a second extended-reality (XR) headset relative to stereo tracking cameras located on a first XR headset based on first tracking information from the stereo tracking cameras. The camera tracking system determines a second pose of eyes of a user wearing the second XR headset relative to the stereo tracking cameras located on the first XR headset based on second tracking information from the stereo tracking cameras. The camera tracking system also calibrates an eye-to-display relationship defining pose of the eyes of the user wearing the second XR headset to a display device of the second XR headset based on the determined first and second poses. The camera tracking system also controls where symbols are displayed on the display device of the second XR headset based on the eye-to-display relationship.

An unauthorized visitor system collects an image of a person detected in a room of a patient. The system identifies reference points on the person's face, for example, points along the cheeks, jowls, and/or brow. The system may compare the reference points to reference points of images associated with registered visitors. The system then determines, based on the comparison, if the person is a registered visitor. One or more designated recipients may be alerted if the person is not a registered visitor or if the person breaches a patient identification zone established around a particular patient. The system may also register the person in a database of visitors.

A method for displaying virtual content to a user, the method includes determining an accommodation of the user's eyes. The method also includes delivering, through a first waveguide of a stack of waveguides, light rays having a first wavefront curvature based at least in part on the determined accommodation, wherein the first wavefront curvature corresponds to a focal distance of the determined accommodation. The method further includes delivering, through a second waveguide of the stack of waveguides, light rays having a second wavefront curvature, the second wavefront curvature associated with a predetermined margin of the focal distance of the determined accommodation.

Systems and methods are disclosed for tracking objects in a physical environment using visual sensors onboard an autonomous unmanned aerial vehicle (UAV). In certain embodiments, images of the physical environment captured by the onboard visual sensors are processed to extract semantic information about detected objects. Processing of the captured images may involve applying machine learning techniques such as a deep convolutional neural network to extract semantic cues regarding objects detected in the images. The object tracking can be utilized, for example, to facilitate autonomous navigation by the UAV or to generate and display augmentative information regarding tracked objects to users.

An information processing apparatus supplies, an image display apparatus including an image capturing unit configured to capture an image of a real space, and a display unit configured to display an image generated using the image captured by the image capturing unit, an image generated using the image captured by the image capturing unit. The information processing apparatus includes a generation unit configured to generate an image depicting a specific object at a position at which the specific object is estimated to be present after a predetermined time from a time when the image display apparatus starts to move in the captured image of the real space including the specific object, and a control unit configured to shift a position at which the image generated by the generation unit is displayed on the display unit based on a change in a position and/or an orientation of the image display apparatus.

Disclosed are methods and apparatuses for image data encoding/decoding. A method of decoding an image includes receiving a bitstream in which the image is encoded; obtaining index information for specifying a block division type of a current block in the image; and determining the block division type of the current block from a candidate group pre-defined in the decoding apparatus. The candidate group includes a plurality of candidate division types, including at least one of a non-division, a first quad-division, a second quad-division, a binary-division or a triple-division. The method also includes dividing the current block into a plurality of sub-blocks; and decoding each of the sub-blocks with reference to syntax information obtained from the bitstream.

A method for efficiently populating a display is provided. The method can include identifying a point at which a world ray intersects a capture surface defined by capture points of a scene, identifying a capture point closest to the identified point, generating a motion vector based on the motion vectors for each of two directly adjacent capture points, identifying a vector in the generated motion vector at a location at which the world ray intersects an image surface, and providing a pixel value from the image data of the capture point, the pixel value corresponding to a location in the image surface at which a vector of the generated motion vector points to the location at which the world ray intersects the image surface within a threshold distance or after a specified number of iterations.

A processing device generates composite images from a sequence of images. The composite images may be used as frames of video. A foreground/background segmentation is performed at selected frames to extract a plurality of foreground object images depicting a foreground object at different locations as it moves across a scene. The foreground object images are stored to a foreground object list. The foreground object images in the foreground object list are overlaid onto subsequent video frames that follow the respective frames from which they were extracted, thereby generating a composite video.

A collision avoidance system comprises a pair of video cameras mounted to a vertical stabilizer of the aircraft, a machine vision processing unit, and a system to inform the pilots of a potential collision. The machine vision processing unit is configured to process image data captured by the video cameras using stereoscopic and structure from motion techniques to detect an obstacle that is near or in the path of the aircraft. Estimates of the range to the object and the rate of change of that range are computed. With the range and range rate, a time to collision can be estimated toward every point of the aircraft. A pilot warning can be sounded based on the nearness of the potential collision. A method of calibrating the video cameras using existing feature points on the top of the aircraft is initiated in response to power being turned on.

A system and method are provided for determining the position and orientation of an implement on a work machine in a non-contact manner using machine vision. A 3D camera, which is mounted on the vehicle with a field of view that includes components on the implement (e.g., markers in some examples), determines a three-dimensional position in a local coordinate system of each of the components. A global positioning system in cooperation with an inertial measurement unit determines a three-dimensional position and orientation of the 3D camera in a global coordinate system. A computing system calculates a three-dimensional position in the global coordinate system for the components using the local three-dimensional positions of the components and the global three-dimensional position and orientation of the 3D camera. The position and orientation of the implement can then be calculated based on the calculated global three-dimensional positions of the components.