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)
Camera motion estimation from observed scene features is an important task in image processing to increase the accuracy of many methods, e.g., optical flow and structure-from-motion. Due to the curved geometry of the state space $${\text {SE}}_{3}$$ SE 3 and the nonlinear relation to the observed optical flow, many recent filtering approaches use a(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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