Abstract: An apparatus for estimating a camera pose according to an embodiment of the present disclosure includes a similar image searcher, a clusterer, and an estimator. The similar image searcher searches for a plurality of images similar to an input image, from among a plurality of previously-stored images, based on the input image. The clusterer creates a cluster including at least some similar images meeting predetermined conditions, from among the plurality of similar images, based on viewpoint data tagged to each of the plurality of similar images. The estimator estimates a pose of a camera that has generated the input image, based on the cluster.

Abstract: An image processing system according to an embodiment includes a spatial map server that generates a spatial map by using a point cloud and viewpoint videos from first real space images obtained by scanning real space, a location recognition server that stores location recognition data extracted from the spatial map, and compares the location recognition data with a second real space image obtained through a device of an AR user to identify location information of the device of the AR user on the spatial map, and a communication server that stores and provides the location information of the device of the AR user on the spatial map and a location of a VR user on the spatial map, and displays at least one or more AR users and at least one VR user on the spatial map in synchronization with each other by using the location information and the location.

Abstract: A system for rendering 6 degree-of-freedom virtual reality according to an embodiment of the present disclosure includes a visibility test module performing a visibility test for determining whether a current point of interest where a main viewpoint is directed is visible for each of a plurality of reference viewpoints and generating visibility information by identifying the number of invisible fragments of each reference viewpoint according to the performance result, a reference viewpoint selection module selecting a final reference viewpoint for a rendering process for a current frame based on the visibility information for each of the plurality of reference viewpoints and a preset selection criterion, and a rendering process module performing an image-based rendering process by using a color image and a depth image corresponding to the final reference viewpoint.

Abstract: A system for rendering 6 degree-of-freedom virtual reality according to an embodiment of the present disclosure includes a visibility test module performing a visibility test for determining whether a current point of interest where a main viewpoint is directed is visible for each of a plurality of reference viewpoints and generating visibility information by identifying the number of invisible fragments of each reference viewpoint according to the performance result, a reference viewpoint selection module selecting a final reference viewpoint for a rendering process for a current frame based on the visibility information for each of the plurality of reference viewpoints and a preset selection criterion, and a rendering process module performing an image-based rendering process by using a color image and a depth image corresponding to the final reference viewpoint.

Abstract: An apparatus for estimating a camera pose according to an embodiment of the present disclosure includes a similar image searcher, a clusterer, and an estimator. The similar image searcher searches for a plurality of images similar to an input image, from among a plurality of previously-stored images, based on the input image. The clusterer creates a cluster including at least some similar images meeting predetermined conditions, from among the plurality of similar images, based on viewpoint data tagged to each of the plurality of similar images. The estimator estimates a pose of a camera that has generated the input image, based on the cluster.

Abstract: An artificial intelligence (AI) system is disclosed that uses machine learning to classify retinal features contained in OCTA data acquired by a data acquisition system and to predict one or more retinopathies based on the classification of retinal features. The AI system comprise a processor configured to run a classifier model comprising a machine learning algorithm and a memory device that stores the classifier model, OCTA training data and acquired OCT A data. The machine learning algorithm performs a process that trains the classifier model to classify retinal features contained in OCT A training data.

Abstract: A multi-modal functional OCT imaging methodology and system are provided that enable concurrent intrinsic optical signal (IOS) imaging of stimulus-evoked neural activity and hemodynamic responses at capillary resolution. An OCT angiography (OCTA)-guided IOS analysis is used to separate neural-IOS and hemodynamic-IOS changes in the same retinal image sequence. The OCTA-guided IOS data processing used for this purpose differentiates two functional images, namely, a neural-IOS map and a hemodynamic-IOS map, from the same image dataset.