Virtual Reconstruction and Morphological Analysis of the Cranium of an Ancient Egyptian Mummy

A mummy of an Egyptian priestess dating from the 22nd dynasty (c. 770 BC), completely enclosed in an anthropoid (human shaped) coffin, was scanned on a CT scanner. An accurate reconstruction of the cranium was generated from 115 × 2 mm CT images using AVS/Express on a SGI computer. Linear measurements were obtained from six orthogonal cranial views and used in a morphometric analysis software package (CRANID). The analyses carried out were both linear and nearest neighbour discriminant analysis. The results show that there is a 52.9% probability that the mummy is an Egyptian female, with a 24.5% probability that mummy is an African female. Thus the technique confirms that the coffin contains an Egyptian female, which is consistent with the inscription on the coffin and the shape of the pelvic bones as revealed by plain X-rays. These results show that this technique has potential for analysing forensic cases where the bones are obscured by soft tissue and clothing. This technique may have an application in virtual autopsies

Neural View-Interpolation for Sparse Light Field Video

We suggest representing light field (LF) videos as "one-off" neural networks (NN), i.e., a learned mapping from view-plus-time coordinates to high-resolution color values, trained on sparse views. Initially, this sounds like a bad idea for three main reasons: First, a NN LF will likely have less quality than a same-sized pixel basis representation. Second, only few training data, e.g., 9 exemplars per frame are available for sparse LF videos. Third, there is no generalization across LFs, but across view and time instead. Consequently, a network needs to be trained for each LF video. Surprisingly, these problems can turn into substantial advantages: Other than the linear pixel basis, a NN has to come up with a compact, non-linear i.e., more intelligent, explanation of color, conditioned on the sparse view and time coordinates. As observed for many NN however, this representation now is interpolatable: if the image output for sparse view coordinates is plausible, it is for all intermediate, continuous coordinates as well. Our specific network architecture involves a differentiable occlusion-aware warping step, which leads to a compact set of trainable parameters and consequently fast learning and fast execution

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