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The major theme of the program is to identify challenges facing the work toward a unifying theoretical foundation of stochastic image grammars. The program has research topics that deals with stochastic image grammars. The topics include learning vocabularies of visual primitives and parts; hierarchical models of events, activities, and scenes; taxonomies and ontologies for activities; inference algorithm...
This paper studies a framework for matching an unknown number of corresponding structures in two images (shapes), motivated by detecting objects in cluttered background and learning parts from articulated motion. Due to the large distortion between shapes and ambiguity caused by symmetric or cluttered structures, many inference algorithms often get stuck in local minimums and converge slowly. We propose...
In this paper, we present an attribute graph grammar for image parsing on scenes with man-made objects, such as buildings, hallways, kitchens, and living moms. We choose one class of primitives - 3D planar rectangles projected on images and six graph grammar production rules. Each production rule not only expands a node into its components, but also includes a number of equations that constrain the...
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