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Real-world scene understanding requires recognizing object categories in novel visual scenes. This paper describes a composition system that automatically learns structured, hierarchical object representations in an unsupervised manner without requiring manual segmentation or manual object localization. A central concept for learning object models in the(More)
The complexity of real world image categorization and scene analysis requires compositional strategies for object representation. This contribution establishes a composi-tional hierarchy by first performing a perceptual bottom-up grouping of edge pixels to generate salient contour curves. A subsequent recursive top-down grouping yields a hierarchy of(More)
Category-level object recognition, segmen-tation, and tracking in videos becomes highly challenging when applied to sequences from a hand-held camera that features extensive motion and zooming. An additional challenge is then to develop a fully automatic video analysis system that works without manual initialization of a tracker or other human intervention(More)