Paper
1 May 1994 Properties of multiscale morphological filters, namely, the morphology decomposition theorem
Pierre Chardaire, J. Andrew Bangham, C. Jeremy Pye, DeQuan Wu
Author Affiliations +
Proceedings Volume 2180, Nonlinear Image Processing V; (1994) https://doi.org/10.1117/12.172552
Event: IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, 1994, San Jose, CA, United States
Abstract
Sieves decompose 1D bounded functions, e.g., f to a set of increasing scale granule functions {dm, m equals 1 ...R}, that represent the information in a manner that is analogous to the pyramid of wavelets obtained by linear decomposition. Sieves based on sequences of increasing scale open-closings with flat structuring elements (M and N filters) map f to {d} and the inverse process maps {d} to f. Experiments show that a more general inverse exists such that {d} maps to f and back to {d}, where the granule functions {d}, are a subset of {d} in which granules may have changed amplitudes, that may include zero but not a change of sign. An analytical proof of this inverse is presented. This key property could prove important for feature recognition and opens the way for an analysis of the noise resistance of these sieves. The resulting theorems neither apply to parallel open-closing filters nor to median based sieves, although root median sieves do `nearly' invert and offer better statistical properties.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pierre Chardaire, J. Andrew Bangham, C. Jeremy Pye, and DeQuan Wu "Properties of multiscale morphological filters, namely, the morphology decomposition theorem", Proc. SPIE 2180, Nonlinear Image Processing V, (1 May 1994); https://doi.org/10.1117/12.172552
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image filtering

Digital filtering

Nonlinear filtering

Nonlinear image processing

Image segmentation

Silicon

Wavelets

Back to Top