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Mathematical Morphology

Proceedings of the VIth International Symposium: ISMM 2002
Authors: Hugues Talbot; Richard Beare;

Mathematical Morphology

Abstract

Mathematical morphology is a powerful methodology for processing and analysing the shape and form of objects in images. The advances in this area of science allow for application in the digital recognition and modeling of faces and other objects by computers. Mathematical Morphology is comprehensive work that provides a broad sampling of the most recent theoretical and practical developments in applications to image processing and analysis. Subject areas covered include: binary morphology, regularised region growing, morphological scale-space techniques, levelings, reconstruction, modeling and simulation, and applications as diverse as medicine, forestry and geology. This fascinating research will be of great interest to engineers, computer scientists, mathematicians and statisticians whose research work is focussed on the theoretical and practical aspects of non-linear image processing and analysis. The content stems from the proceedings of the VIth International Symposium on Mathematical Morphology, held April 3–5, 2002 in Sydney, Australia.

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    Top 10%
    influence
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Top 10%
Average
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