In the past few years, practical distributed video coding systems have been proposed based on Slepian-Wolf and
Wyner-Ziv theorems. The quality of side information plays a critical role in the overall performance for such a system.
In this paper, we present a novel approach to generating the side information using optimal filtering techniques. The
motion vectors (MVs) that define the motion activity between the main information and the side information are first
predicted by an optimal filter, then the MVs obtained from a decoded WZ frame by a conventional motion search
method corrects the prediction results. The side information is generated from the updated MVs via a motion
compensated interpolation (MCI) process and can be subsequently fed into the decoding process to further improve the
quality of a decoded WZ frame. We studied several variations of optimal filters and compared them with other DVC
systems in terms of rate-distortion performance.
In this paper, we describe a shape space based approach for invariant object representation and recognition. In this approach, an object and all its similarity transformed versions are identified with a single point in a high-dimensional manifold called the shape space. Object recognition is achieved by measuring the geodesic distance between an observed object and a model in the shape space. This approach produced promising results in 2D object recognition experiments: it is invariant to similarity transformations and is relatively insensitive to noise and occlusion. Potentially, it can also be used for 3D object recognition.
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