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.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/30400 |
| Date | 08 August 2003 |
| Creators | Felzenszwalb, Pedro F. |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 80 p., 38103057 bytes, 1889641 bytes, application/postscript, application/pdf |
| Relation | Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory |
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