Abstract
Connectivity is a topological notion for sets, often introduced by means of arcs. Classically, discrete geometry transposes to digital sets this arcwise appoach. An alternative, and non topological, axiomatics has been proposed by Serra. It lies on the idea that the union of connected components that intersect is still connected. Such an axiomatics enlarges the range of possible connections, and includes clusters of particles.
The main output of this approach concerns filters. Very powerful new ones have been designed (levelings), and more classical ones have been provided with new properties (openings, strong alternated filters)
The paper presents an overview of set connection and illustrates it by filterings on gray tone images. It is emphazised that all notions introduced here apply equally to both discrete and continuous spaces.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Matheron G. and Serra J. Strong filters and connectivity.Ch 7 in Image Analysis and Mathematical Morphology. Vol. 2. Serra J. ed. London: Acad. Press (1988), pp. 141–157.
Serra J. Image Analysis and Mathematical Morphology. Vol. 2. Serra J. ed. London: Acad. Press, 1988.
Serra J. and Salembier P. Connected operators and pyramids. In SPIE, Vol. 2030, Non linear algebra and morphological image processing, San Diego, July 1993, pp. 65–76.
Ronse C. Set theoretical algebraic approaches to connectivity in continuous or digital spaces. JMIV, 1998, Vol. 8, pp. 41–58.
Haralick R.M. and Shapiro L.G. Computer and robot vision. Vol. I. Addison Wesley, 1992, pp. 191–198.
Serra J. Connectivity on complete latticesJMIV, 1998, Vol. 9, pp. 231–251
Heijmans, M.J.A.M. Connected morphological operators. Tech. Rep. CWI No PNA-R9708, April 1997.
Matheron G. Les nivellements. Tech. Rep. Ecole des Mines de Paris, No N-07/97/MM, Feb. 1997.
Meyer F. The levelings. In Mathematical Morphology and its applications to image and signal processing, H. Heijmans and J. Roerdinkeds., Kluwer, 1998.
Crespo, J. Schafer, R.W. Locality and adjacency stability constraints for morphological connected operators. Journal of Mathematical Imaging and Vision7, 1 (1997), 85–102.
Serra, J. Connectivity for sets and functions to appear in fundamenta Informaticae, Aug.1998
Matheron G. Filters and Lattices. Ch 6 in Image Analysis and Mathematical Morphology. Vol. 2. Serra J. ed.London: Acad. Press, (1988), pp.115–140.
Meyer F. Minimum spanning forests for morphological segmentation. In Mathematical Morphology and its applications to image and signal processing, Maragos P. et al. eds., Kluwer,1996.
Marcotegui B. and Meyer F. Morphological segmentation of image sequences. In Mathematical Morphology and its applications to image processing, Serra J. and Soille P. eds., Kluwer, 1994, pp. 101–108.
Salembier P. and Oliveras A. Practical extensions of connected operators. In Mathematical Morphology and its applications to image and signal processing, Maragos P. et al., eds. Kluwer, 1996, pp. 97–110.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Serra, J. (1999). Set Connections and Discrete Filtering. In: Bertrand, G., Couprie, M., Perroton, L. (eds) Discrete Geometry for Computer Imagery. DGCI 1999. Lecture Notes in Computer Science, vol 1568. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49126-0_15
Download citation
DOI: https://doi.org/10.1007/3-540-49126-0_15
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65685-2
Online ISBN: 978-3-540-49126-2
eBook Packages: Springer Book Archive