Maybank, Stephen J. and Ieng, S. and Benosman, R. (2012) A Fisher-Rao metric for paracatadioptric images of lines. International Journal of Computer Vision 99 (2), pp. 147-165. ISSN 0920-5691.
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Abstract
In a central paracatadioptric imaging system a perspective camera takes an image of a scene reflected in a paraboloidal mirror. A 360° field of view is obtained, but the image is severely distorted. In particular, straight lines in the scene project to circles in the image. These distortions make it diffcult to detect projected lines using standard image processing algorithms. The distortions are removed using a Fisher-Rao metric which is defined on the space of projected lines in the paracatadioptric image. The space of projected lines is divided into subsets such that on each subset the Fisher-Rao metric is closely approximated by the Euclidean metric. Each subset is sampled at the vertices of a square grid and values are assigned to the sampled points using an adaptation of the trace transform. The result is a set of digital images to which standard image processing algorithms can be applied. The effectiveness of this approach to line detection is illustrated using two algorithms, both of which are based on the Sobel edge operator. The task of line detection is reduced to the task of finding isolated peaks in a Sobel image. An experimental comparison is made between these two algorithms and third algorithm taken from the literature and based on the Hough transform.
Metadata
| Item Type: | Article |
|---|---|
| Additional Information: | The final publication is available at Springer via http://dx.doi.org/10.1007/s11263-012-0523-x |
| Keyword(s) / Subject(s): | central projection, Fisher-Rao metric, Hough transform, line detection, paraboloidal mirror, paracatadioptric system, Sobel operator, trace transform. |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Professor Stephen Maybank |
| Date Deposited: | 30 May 2014 12:20 |
| Last Modified: | 09 Aug 2023 12:35 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/9833 |
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