This research addresses the problems of detection and recognition of deformable articulated shapes, such as people and animals, from real images. The aim is to develop a Top-down and Bottom-up recognition strategy, where the Bottom-up detects features, group them, and access the object model, while the Top-down uses the object model to search for features. Objects are represented in a tree-graph structure derived from a shape axis model. The information stored in the nodes include probabilities, used to specify likely geometric deformations of the object and of the imaging function. The tree-graph structure allows for a description of occlusions and articulations in terms of tree matching operations. To further reduce the computational complexity of the task, an information theory criteria is studied to determine features which convey information about the objects in the image. The PI will also develop and teach two courses, one of them new to NYU, to support a focused program in computer vision; these course will be``Introduction to Vision'', and a specialized course in ``Grouping and Recognition''.
