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We cast some new insights into solving the digital matting problem by treating it as a semi-supervised learning task in machine learning. A local learning based approach and a global learning based approach are then produced, to fit better the scribble based matting and the trimap based matting, respectively. Our approaches are easy to implement because only some simple matrix operations are needed...
Background modeling is an essential and important part of many high-level video processing applications. Recently, the Support Vector Data Description (SVDD) has been introduced for novelty detection when only one class of data is available, i.e. background pixels. This paper proposes a method to efficiently train an SVDD and compares the performance of this training algorithm with the traditional...
Parameterized appearance models (PAMs) (e.g. eigen-tracking, active appearance models, morphable models) use principal component analysis (PCA) to model the shape and appearance of objects in images. Given a new image with an unknown appearance/shape configuration, PAMs can detect and track the object by optimizing the modelpsilas parameters that best match the image. While PAMs have numerous advantages...
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