Mathematical morphology is a theory introduced more than 40 years ago. Since
then, it has been widely used in the fields of image analysis and processing,
due to its ability to analyse spatial structures (most often by means of a
neighborhood called structuring element) in a non-linear framework. Its
application to binary and greylevel images is straightforward, relying on the
set theory or preferably the lattice theory. However, its extension to color or
other multivalued images (where each pixel is represented by a vector instead
of
a scalar) is not trivial and is still an open problem. Once such an extension
is
available, a large panel of problems may be addressed by morphological
approaches: template matching, image segmentation, image description, etc. In
this talk, I will address this problem and illustrate our contributions by some
of our methodological achievements in color and multispectral image analysis.