A system and method for reviewing and editing a series of time lapse photographs, using a machine learning system to review sequentially the individual photographs in the series, identify features in the photographs which features may have been classified as undesirable and flag an individual photograph as undesirable, remove photographs flagged as undesirable from the series set, review the remaining images from the series set of photographs for lighting and composition characteristics and further selection, process the selected photographs for image stabilization, and assembling the processed photographs into a single video for viewing.

A method may include obtaining first image data relating to a region of interest (ROI) of a first subject. The first image data corresponding to a first equivalent dose level may be acquired by a first device. The method may also include obtaining a model for denoising relating to the first image data and determining second image data corresponding to an equivalent dose level higher than the first equivalent dose level based on the first image data and the model for denoising. In some embodiments, the method may further include determining information relating to the ROI of the first subject based on the second image data and recording the information relating to the ROI of the first subject.

A method may include obtaining first image data relating to a region of interest (ROI) of a first subject. The first image data corresponding to a first equivalent dose level may be acquired by a first device. The method may also include obtaining a model for denoising relating to the first image data and determining second image data corresponding to an equivalent dose level higher than the first equivalent dose level based on the first image data and the model for denoising. In some embodiments, the method may further include determining information relating to the ROI of the first subject based on the second image data and recording the information relating to the ROI of the first subject.

Disclosed are a method and system for testing a wearable device. The method includes: performing an angle acquisition process for at least two times, and calculating an optical imaging parameter value of a target virtual image on the basis of angle variation values acquired in the at least two angle acquisition processes. With the method and system according to the present disclosure, the finally calculated optical imaging parameter value is more objective and more accurate than that acquired by means of the human eyes.

A vision-based navigation system (e.g., for aircraft on approach to a runway) captures via camera 2D images of the runway environment in an image plane. The vision-based navigation system stores a constellation database of runway features and their nominal 3D position information in a constellation plane. Image processors detect within the captured images 2D features potentially corresponding to the constellation features. The vision-based navigation system estimates optical pose of the camera in the constellation plane by aligning the image plane and constellation plane into a common domain, e.g., via orthocorrection of detected image features into the constellation plane or reprojection of constellation features into the image plane. Based on the common-domain plane, the vision-based navigational system generates candidate correspondence maps (CMAP) of constellation features mapped to the image features with high-confidence error bounding, from which optical pose of the camera or aircraft can be estimated.

A vision-based navigation system (e.g., for aircraft on approach to a runway) captures via camera 2D images of the runway environment in an image plane. The vision-based navigation system stores a constellation database of runway features and their nominal 3D position information in a constellation plane. Image processors detect within the captured images 2D features potentially corresponding to the constellation features. The vision-based navigation system estimates optical pose of the camera in the constellation plane by aligning the image plane and constellation plane into a common domain, e.g., via orthocorrection of detected image features into the constellation plane or reprojection of constellation features into the image plane. Based on the common-domain plane, the vision-based navigational system generates candidate correspondence maps (CMAP) of constellation features mapped to the image features with high-confidence error bounding, from which optical pose of the camera or aircraft can be estimated.

Example implementations described herein involve systems and methods for a mobile application device to playback and record augmented reality (AR) overlays indicating gestures to be made to a recorded device screen. A device screen is recorded by a camera of the mobile device, wherein a mask is overlaid on a user hand interacting with the device screen. Interactions made to the device screen are detected based on the mask, and AR overlays are generated corresponding to the reactions.

Aspects of the disclosure provide methods and apparatuses for mesh coding (encoding and/or decoding). In some examples, an apparatus for coding mesh includes processing circuitry. The processing circuitry decodes, three dimensional (3D) coordinates of vertices in a first 3D mesh frame and connectivity information of the vertices from a bitstream that carries the first 3D mesh frame. The first 3D mesh frame represents a surface of an object with polygons. The processing circuitry deduces texture coordinates associated with the vertices, and decodes a texture map for the first 3D mesh frame from the bitstream. The texture map includes first one or more 2D charts with 2D vertices having the texture coordinates. The processing circuitry reconstructs the first 3D mesh frame based on the 3D coordinates of the vertices, the connectivity information of the vertices, the texture map and the texture coordinates.

Aspects of the disclosure provide methods and apparatuses for mesh coding (encoding and/or decoding). In some examples, an apparatus for coding mesh includes processing circuitry. The processing circuitry decodes, three dimensional (3D) coordinates of vertices in a first 3D mesh frame and connectivity information of the vertices from a bitstream that carries the first 3D mesh frame. The first 3D mesh frame represents a surface of an object with polygons. The processing circuitry deduces texture coordinates associated with the vertices, and decodes a texture map for the first 3D mesh frame from the bitstream. The texture map includes first one or more 2D charts with 2D vertices having the texture coordinates. The processing circuitry reconstructs the first 3D mesh frame based on the 3D coordinates of the vertices, the connectivity information of the vertices, the texture map and the texture coordinates.

Responsive to a zoom command when presenting a first video, a second video is combined with the first video and presented. The first and second videos are generated from substantially the same camera location as each other at substantially the same time with substantially the same resolution. However, the second video is generated by a physical or virtual lens having a field of view (FOV) smaller than the FOV of a physical or virtual lens used in generating the first video. Modules are described for using alignment metrics to correctly place the second video over the inner video and make it appear seamless.