| dc.creator | Felzenszwalb, Pedro F. | |
| dc.date | 2004-10-20T20:32:11Z | |
| dc.date | 2004-10-20T20:32:11Z | |
| dc.date | 2003-08-08 | |
| dc.date.accessioned | 2013-10-09T02:48:24Z | |
| dc.date.available | 2013-10-09T02:48:24Z | |
| dc.date.issued | 2013-10-09 | |
| dc.identifier | AITR-2003-016 | |
| dc.identifier | http://hdl.handle.net/1721.1/7111 | |
| dc.identifier.uri | http://koha.mediu.edu.my:8181/xmlui/handle/1721 | |
| dc.description | 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. | |
| dc.format | 80 p. | |
| dc.format | 6877524 bytes | |
| dc.format | 3132998 bytes | |
| dc.format | application/postscript | |
| dc.format | application/pdf | |
| dc.language | en_US | |
| dc.relation | AITR-2003-016 | |
| dc.subject | AI | |
| dc.title | Representation and Detection of Shapes in Images |
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