Andreas Neufeld

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Accurate camera motion estimation is a fundamental building block for many Computer Vision algorithms. For improved robust-ness, temporal consistency of translational and rotational camera velocity is often assumed by propagating motion information forward using stochastic filters. Classical stochastic filters, however, use linear approximations for the(More)
We propose a variational approach for estimating egomotion and structure of a static scene from a pair of images recorded by a single moving camera. In our approach the scene structure is described by a set of 3D planar surfaces, which are linked to a SLIC superpixel decomposition of the image domain. The continuously parametrized planes are determined(More)
This work studies the fundamental building blocks for steganography in H.264 compressed video: the embedding operation and the choice of embedding locations. Our aim is to inform the design of better video steganography, a topic on which there has been relatively little publication so far. We determine the best embedding option, from a small menu of(More)
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