Abstract:
Disclosed herein are an apparatus and method for 360-degree video stitching. The apparatus for 360-degree video stitching includes memory for storing at least one program, and a processor for executing the program, wherein the program is configured to stitch features of multiple input images based on at least one parameter included in a 360-degree stitching function description template, and then creating a single 360-degree video, the 360-degree stitching function description template includes a configuration parameter that is an array of function parameters, the configuration parameter includes a stitching parameter, a camera parameter, a feature parameter, and a projection parameter, the feature parameter includes a method for extracting respective features from multiple input images, and the projection parameter includes a projection type that is a kind of a projection plane onto which the multiple input images are projected.
Abstract:
A system for providing a virtual desktop infrastructure (VDI) service includes: a service provider configured to provide VDI service data to a client terminal; and a watermark inserter configured to insert a watermark into the VDI service data, in which the watermark comprises a watermark code for identifying a watermark and a terminal code for identifying a client terminal.
Abstract:
A method for dynamically selecting multiple cameras to track a target object, the method including selecting a main camera from among multiple cameras; selecting a target object from an image captured by the main camera; projecting a captured location of the target object onto images to be captured by one or more sub cameras; and selecting sub cameras according to a pixel proportion that indicates a number of pixels which are included in a capture location of the target object in the images captured by the one or more sub cameras.
Abstract:
Disclosed herein are a method and apparatus for generating a panoramic image. The method includes configuring a data set for training a deep learning network based on K images, extracting an encoding feature map, a skip connection feature map, and a decoding feature map based on K/2 images, among the images, estimating multiple homographies based on the encoding feature map, estimating a flow adjustment map, a preprocessing blending map, a weight map, and a post-processing blending map based on the decoding feature map, deriving a flow map based on the multiple homographies and the flow adjustment map, generating a preprocessed image based on the preprocessing blending map, generating multiple warped images based on the preprocessed image and the flow map, generating a matching image based on the warped images and the weight map, and correcting the matching image based on the matching image and the post-processing blending map.