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, 177. Our method follows the texture r ecognition work of De Bonet and Viola 4. We use feature vectors which capture the joint occurrence o f l o cal features at multiple resolutions. The distribution of(More)
ENG distribute publicly paper and electronic copies of this thesis document in whole or in part. Abstract 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.(More)
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