Representation and Detection of Shapes in Images
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 proper...
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| Language: | en_US |
| Published: |
2004
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| Online Access: | http://hdl.handle.net/1721.1/7111 |
| _version_ | 1811093094599229440 |
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| author | Felzenszwalb, Pedro F. |
| author_facet | Felzenszwalb, Pedro F. |
| author_sort | Felzenszwalb, Pedro F. |
| collection | MIT |
| 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. |
| first_indexed | 2024-09-23T15:39:40Z |
| id | mit-1721.1/7111 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:39:40Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/71112019-04-12T08:33:58Z Representation and Detection of Shapes in Images Felzenszwalb, Pedro F. AI 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. 2004-10-20T20:32:11Z 2004-10-20T20:32:11Z 2003-08-08 AITR-2003-016 http://hdl.handle.net/1721.1/7111 en_US AITR-2003-016 80 p. 6877524 bytes 3132998 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | AI Felzenszwalb, Pedro F. Representation and Detection of Shapes in Images |
| title | Representation and Detection of Shapes in Images |
| title_full | Representation and Detection of Shapes in Images |
| title_fullStr | Representation and Detection of Shapes in Images |
| title_full_unstemmed | Representation and Detection of Shapes in Images |
| title_short | Representation and Detection of Shapes in Images |
| title_sort | representation and detection of shapes in images |
| topic | AI |
| url | http://hdl.handle.net/1721.1/7111 |
| work_keys_str_mv | AT felzenszwalbpedrof representationanddetectionofshapesinimages |