Abstract
The one-dimensional image analysis method know as the sieve[1] is extended to any finite dimensional image. It preserves all the usual scale-space properties but has some additional features that, we believe, make it more attractive than the diffusion-based methods. We present some simple examples of how it might be used.
Cite
CITATION STYLE
APA
Andrew Bangham, J., Harvey, R., Ling, P. D., & Aldridge, R. V. (1996). Nonlinear scale-space from n-dimensional sieves. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1064, pp. 189–198). Springer Verlag. https://doi.org/10.1007/bfb0015535
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