Representation and Detection of Shapes in Images
dc.date.accessioned | 2004-10-20T20:32:11Z | |
dc.date.accessioned | 2018-11-24T10:23:08Z | |
dc.date.available | 2004-10-20T20:32:11Z | |
dc.date.available | 2018-11-24T10:23:08Z | |
dc.date.issued | 2003-08-08 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/7111 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/7111 | |
dc.description.abstract | We present a set of techniques that can be used to represent and detect shapes in images. Our methods revolve around a particular shape representation based on the description of objects using triangulated polygons. This representation is similar to the medial axis transform and has important properties from a computational perspective. The first problem we consider is the detection of non-rigid objects in images using deformable models. We present an efficient algorithm to solve this problem in a wide range of situations, and show examples in both natural and medical images. We also consider the problem of learning an accurate non-rigid shape model for a class of objects from examples. We show how to learn good models while constraining them to the form required by the detection algorithm. Finally, we consider the problem of low-level image segmentation and grouping. We describe a stochastic grammar that generates arbitrary triangulated polygons while capturing Gestalt principles of shape regularity. This grammar is used as a prior model over random shapes in a low level algorithm that detects objects in images. | en_US |
dc.format.extent | 80 p. | en_US |
dc.format.extent | 6877524 bytes | |
dc.format.extent | 3132998 bytes | |
dc.language.iso | en_US | |
dc.subject | AI | en_US |
dc.title | Representation and Detection of Shapes in Images | en_US |
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