Cheng, B., Kay, J. (1995) A Regularization Method for one Dimensional Edge-Dectection and Edge-Preserving Smoothing. Computational Statistics, 10 (1). pp. 53-69. ISSN 0943-4062. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:19758)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. |
Abstract
We consider the problem of detecting discontinuities and estimating an unknown discontinuous function from noisy data. This is an ill-posed inverse problem, which needs to be regularized beyond the conventional dilemma between the fidelity to the data and the degree of the global smoothness which now doesn't exist. In this paper, we introduce a regularization functional having two items. The first is a measure of piecewise-smoothness of the function while the second is penalized by the local components: locations, sizes, and degrees of the discontinuities, and is also controlled by the global Components: the number of discontinuity points and the degree of piecewise-smoothness. We develop a methodology for the problem of edge-preserving smoothing and edge-detection. Two algorithms are proposed and the simulations were run for several one-dimensional synthetic images. We assess the results in the light of some performance criteria described by Canny (1986).
Item Type: | Article |
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Subjects: | H Social Sciences > HA Statistics |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science |
Depositing User: | P. Ogbuji |
Date Deposited: | 05 Jun 2009 18:42 UTC |
Last Modified: | 16 Nov 2021 09:57 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/19758 (The current URI for this page, for reference purposes) |
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