Dense sampling of shape interiors for improved representation
Matching shapes accurately is an important requirement in various applications; the most notable of which is object recognition. Precisely matching shapes is a difficult task and is an active area of research in the computer vision community. Most shape matching techniques rely on the contour of the...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/84242 http://hdl.handle.net/10220/13362 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Matching shapes accurately is an important requirement in various applications; the most notable of which is object recognition. Precisely matching shapes is a difficult task and is an active area of research in the computer vision community. Most shape matching techniques rely on the contour of the object to provide the object's shape properties. However, we show that using the contour alone cannot help in matching all kinds of shapes. Many objects are recognised because of their overall visual similarity, rather than just their contour properties. In this paper, we assert that modelling the interior properties of the shape can help in extracting this overall visual similarity. We propose a simple way to extract the shape's interior properties. This is done by densely sampling points from within the shape and using it to describe the shape's features. We show that using such an approach provides an effective way to perform matching of shapes that are visually similar to each other, but have vastly different contour properties. |
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