Abstract
One of the aims of computer vision in the past 30 years has been to recognize shapes by numerical algorithms. Now, what are the geometric features on which shape recognition can be based? In this paper, we review the mathematical arguments leading to a unique definition of planar shape elements. This definition is derived from the invariance requirement to not less than five classes of perturbations, namely noise, affine distortion, contrast changes, occlusion, and background. This leads to a single possibility: shape elements as the normalized, affine smoothed pieces of level lines of the image. As a main possible application, we show the existence of a generic image comparison technique able to find all shape elements common to two images. © 2003 Society for Industrial and Applied Mathematics.
| Original language | English |
|---|---|
| Pages (from-to) | 1-24 |
| Journal | Multiscale Modeling and Simulation |
| Volume | 1 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2003 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Generic algorithm
- Invariant planar shape recognition
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