Thomas D. Rikert

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This paper presents an approach to object detection which is based on recent work in statistical models for texture synthesis and recognition [7, 4, 23, 17]. Our method follows the texture recognition work of De Bonet and Viola [4]. We use feature vectors which capture the joint occurrence of local features at multiple resolutions. The distribution of(More)
This paper presents preliminary work on a novel technique for gaze estimation from a single image. The goal is to provide rough estimates of where a person is looking at a monitor. Many applications for human-computer interaction are possible for such a technique. Our approach uses the morphable model framework of Jones and Poggio 4] to model a region(More)
Texture is an important cue for detecting objects that undergo shape deformation, pose changes and variations in illumination. We propose a general statistical model which relies on texture for learning an object class from a set of example images. We use the class of human faces to test our ideas. Once the model learns the distribution of the face images,(More)
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