Representation and Detection of Shapes in Images
Item
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Title
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en_US
Representation and Detection of Shapes in Images
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Creator
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en_US
Felzenszwalb, Pedro F.
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Date
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2004-10-20T20:32:11Z
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Date Available
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2004-10-20T20:32:11Z
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Date Issued
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2003-08-08
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Identifier
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AITR-2003-016
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Abstract
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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.
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Extent
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en_US
80 p.
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6877524 bytes
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3132998 bytes
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Format
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application/postscript
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application/pdf
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Language
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en_US
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Relation
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AITR-2003-016
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Subject
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AI