Single-image Tomography: 3D Volumes from 2D Cranial X-Rays

As many different 3D volumes could produce the same 2D x-ray image, inverting this process is challenging. We show that recent deep learning-based convolutional neural networks can solve this task. As the main challenge in learning is the sheer amount of data created when extending the 2D image into a 3D volume, we suggest firstly to learn a coarse, fixed-resolution volume which is then fused in a second step with the input x-ray into a high-resolution volume. To train and validate our approach we introduce a new dataset that comprises of close to half a million computer-simulated 2D x-ray images of 3D volumes scanned from 175 mammalian species. Applications of our approach include stereoscopic rendering of legacy x-ray images, re-rendering of x-rays including changes of illumination, view pose or geometry. Our evaluation includes comparison to previous tomography work, previous learning methods using our data, a user study and application to a set of real x-rays

Systems for the Nineties - Distributed Multimedia Systems

We live at the dawn of the information age. The capabilities of computers to store and look up information are only just beginning to be exploited. As little as ten years ago, practically all the information stored in computers was entered and retrieved in the form of text. Today, we are just starting to use other means of communicating information between people and machines -- computers can now scan images, they can record sound, they can produce synthesized speech, and they can show two- and three-dimensional images of spatial data. The realization that we are still at the beginning of the information age comes when we notice the vast difference between the way in which people interact with each other and the way in which people can interact with (or through) machines. When people communicate, they tend to use speech, gestures, touch, even smell; they draw pictures on the white board, they use text, pictures, photos, graphs, sometimes even video presentations. nterpersonal communication is truly multimedia communication in that it makes use of all our senses

Characterizing and Improving Stability in Neural Style Transfer

Recent progress in style transfer on images has focused on improving the quality of stylized images and speed of methods. However, real-time methods are highly unstable resulting in visible flickering when applied to videos. In this work we characterize the instability of these methods by examining the solution set of the style transfer objective. We show that the trace of the Gram matrix representing style is inversely related to the stability of the method. Then, we present a recurrent convolutional network for real-time video style transfer which incorporates a temporal consistency loss and overcomes the instability of prior methods. Our networks can be applied at any resolution, do not re- quire optical flow at test time, and produce high quality, temporally consistent stylized videos in real-time