Parag Havaldar

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We address the problem of recognition of generic objects from a single intensity image. This precludes the use of purely geometric methods which assume that models are geometrically and precisely designed. Instead, we propose to use descriptions in terms of features and their qualitative geometric relationships. To succeed, it is clear that these features(More)
Synthesizing the image of a 3-D scene as it would be captured by a camera from an arbitrary viewpoint is a central problem in Computer Graphics. Given a complete 3-D model, it is possible to render the scene from any viewpoint. The construction of models is a tedious task. Here, we propose to bypass the model construction phase altogether, and to generate(More)
We present a method to infer segmented and full volumetric descriptions of objects from intensity images. We use three weakly calibrated images from closely spaced viewpoints as input. Deriving full volumetric descriptions requires the development of robust inference rules. The inference rules are based on local properties of generalized cylinders (GCs). We(More)
Modeling a face and rendering it in a manner that appears realistic is a hard problem in itself, and remarkable progress to achieve realistic looking faces has been made from a modeling perspective [1, 6, 13, 15, 16, 2] as well as a rendering perspective [5, 11, 12]. At last years Siggraph 2005, the course of Digital Face Cloning described relevant material(More)
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