Representation and Detection of Shapes in Images

We present a set of techniques that can be used to represent anddetect shapes in images. Our methods revolve around a particularshape representation based on the description of objects usingtriangulated polygons. This representation is similar to the medialaxis transform and has important properties from a computationalperspective. The first problem we consider is the detection ofnon-rigid objects in images using deformable models. We present anefficient algorithm to solve this problem in a wide range ofsituations, and show examples in both natural and medical images. Wealso consider the problem of learning an accurate non-rigid shapemodel for a class of objects from examples. We show how to learn goodmodels while constraining them to the form required by the detectionalgorithm. Finally, we consider the problem of low-level imagesegmentation and grouping. We describe a stochastic grammar thatgenerates arbitrary triangulated polygons while capturing Gestaltprinciples of shape regularity. This grammar is used as a prior modelover random shapes in a low level algorithm that detects objects inimages