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 com­ plete 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)
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 address the problem of recovering high-level, volumetric and segmented (or part-based) descriptions of objects from intensity images. As input we use three closely spaced images of an object and recover descriptions based on Generalized Cylinders (GCCs). We start by extracting a hierarchy of groups from contour images in the three views. Grouping is(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)
Since 1931, the Academy of Motion Picture Arts and Sciences has honored the inventors and developers of the technology behind the movies. Earlier this year, 18 different technologies were awarded, honoring 34 individuals. These technologies have had a significant impact on how movies are made and have stood the test of time.
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