Locality and Adjacency Stability Constraints for Morphological Connected Operators

This paper investigates two constraints for the connected operator class. For binary images, connected operators are those that treat grains and pores of the input in an all or nothing way, and therefore they do not introduce discontinuities. The first constraint, called connected-component (c.c.) locality, constrains the part of the input that can be used for computing the output of each grain and pore. The second, called adjacency stability, establishes an adjacency constraint between connected components of the input set and those of the output set. Among increasing operators, usual morphological filters can satisfy both requirements. On the other hand, some (non-idempotent) morphological operators such as the median cannot have the adjacency stability property. When these two requirements are applied to connected and idempotent morphological operators, we are lead to a new approach to the class of filters by reconstruction. The important case of translation invariant operators and the relationships between translation invariance and connectivity are studied in detail. Concepts are developed within the binary (or set) framework; however, conclusions apply as well to flat non-binary (gray-level) operators.

[1]  L. Marton,et al.  Advances in Electronics and Electron Physics , 1958 .

[2]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[3]  G. Matheron Éléments pour une théorie des milieux poreux , 1967 .

[4]  José Crespo Space Connectivity and Translation-Invariance , 1996, ISMM.

[5]  Petros Maragos,et al.  Morphological filters-Part II: Their relations to median, order-statistic, and stack filters , 1987, IEEE Trans. Acoust. Speech Signal Process..

[6]  Henk J. A. M. Heijmans,et al.  Theoretical Aspects of Gray-Level Morphology , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Jean Serra Anamorphoses and function lattices , 1993, Optics & Photonics.

[8]  Petros Maragos,et al.  Morphological filters-Part I: Their set-theoretic analysis and relations to linear shift-invariant filters , 1987, IEEE Trans. Acoust. Speech Signal Process..

[9]  Philippe Salembier,et al.  Connected operators and pyramids , 1993, Optics & Photonics.

[10]  C. Lantuéjoul,et al.  On the use of the geodesic metric in image analysis , 1981 .

[11]  H. Heijmans,et al.  The algebraic basis of mathematical morphology , 1988 .

[12]  Edward R. Dougherty,et al.  Morphological methods in image and signal processing , 1988 .

[13]  J. Crespo Morphological connected filters and intra-region smoothing for image segmentation , 1993 .

[14]  Harvard Univer A Representation Theory for Morphological Image and Signal Processing , 1989 .

[15]  G. Matheron Random Sets and Integral Geometry , 1976 .

[16]  R. Schafer,et al.  Morphological systems for multidimensional signal processing , 1990, Proc. IEEE.

[17]  José Crespo,et al.  Theoretical aspects of morphological filters by reconstruction , 1995, Signal Process..