50,385 research outputs found
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
Improving Usability of Interactive Graphics Specification and Implementation with Picking Views and Inverse Transformations
Specifying and programming graphical interactions are difficult tasks,
notably because designers have difficulties to express the dynamics of the
interaction. This paper shows how the MDPC architecture improves the usability
of the specification and the implementation of graphical interaction. The
architecture is based on the use of picking views and inverse transforms from
the graphics to the data. With three examples of graphical interaction, we show
how to express them with the architecture, how to implement them, and how this
improves programming usability. Moreover, we show that it enables implementing
graphical interaction without a scene graph. This kind of code prevents from
errors due to cache consistency management
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