Abstract: A system and method for animating an avatar in a virtual world comprising an image processor arranged to process a stream of images capturing an active user to obtain an activity data set arranged to track the activity of the user; an avatar spatial processor arranged to process the activity data set to determine a plurality of motion tracking points arranged to track the user's activity over a three-dimensional space; a facial expression detection engine arranged to process the activity data set to detect one or more facial expressions of the user; and, an avatar animation engine arranged to animate the avatar in the virtual world with the plurality of motion tracking points and the detected one or more facial expressions so as to mirror the actions and facial expressions of the active user.

Abstract: A novel medical image generation approach synthesizes thin-cut computerized tomography (CT) images from thick-cut CT ones as inputs. First thick-cut CT images are obtained by maximizing the pixel-wise intensity of five or more than five continuous thin-cut ones after image registration. Second, the obtained thick-cut CT images are fed into a generator block which adopts an encoder-decoder architecture, where each thick-cut image is encoded into low-dimensional embedding space before decoding into multiple thin-cut ones. Third, a discriminator focuses on distinguishing original real thin-cut images from synthetic thin-cut images. An adversarial mechanism between the generator and discriminator causes the discriminator's output to provide an effective gradient update of the network parameters for the generator to increasingly improve the generator's ability to synthesize higher-quality thin-cut images and in turn promotes the discriminator's discriminating capability.

Abstract: A three dimensional classification system for recognizing cross-sectional images automatically contains a processor that executes: (1) rescaling of a plurality of cross-sectional images; and feeding the rescaled plurality of cross-sectional images into two branches; (2) feeding the rescaled plurality of cross-sectional images into a first branch for performing a plurality of convolutions on the rescaled plurality of cross-sectional images directly to learn features for distinguishing phases; (3) feeding the rescaled plurality of cross-sectional images into a second branch for reducing resolution, and then performing a plurality of convolutions on the reduced resolution plurality of cross-sectional images to learn features for distinguishing phases; and (4) concatenating convolutional output channels from the two branches to fuse global and local features, on which two fully-connected layers are stacked as a classifier to recognize cross-sectional volumetric images accurately and quickly.