STRUCTURALLY ADAPTIVE MATHEMATICAL MORPHOLOGY BASED ON NONLINEAR SCALE-SPACE DECOMPOSITIONS

Structurally Adaptive Mathematical Morphology Based on Nonlinear Scale-space Decompositions

Standard formulation of morphological operators is translation invariant in the space and in the intensity: the same processing is considered for each point of the image. A current challenging topic in mathematical morphology is the construction of adaptive operators. In previous works, the adaptive operators are based either on spatially variable neighbourhoods according to the local regularit...

Morphological Scale Space and Mathematical Morphology

Dynamic scale-space paradigms versus mathematical morphology?

The geometric and statistical physical concepts of dynamic scale-space paradigms are presented and juxtaposed to those of mathematical morphology. It turns out that the dynamic paradigms can be applied to, substantiate and even generalise the morphological techniques and paradigms. In particular the importance of the dynamic scale-space concepts in granulometry by means of size densities or sta...

On-line Stable Nonlinear Modelling by Structurally Adaptive Neural Nets

This paper proposes a neural net based on-line scheme for modelling discrete-time mul-tivariable nonlinear dynamical systems. Taking the advantage of structural features of RBF (Radial-Basis-Function) neural nets, the method approaches the modelling problem by setting up a coarse RBF model structure in the light of the spatial Fourier transform and spatial sampling theory, then devising appropr...

Nonlinear-Adaptive Mathematical System Identification

By reversing paradigms that normally utilize mathematical models as the basis for nonlinear adaptive controllers, this article describes using the controller to serve as a novel computational approach for mathematical system identification. System identification usually begins with the dynamics, and then seeks to parameterize the mathematical model in an optimization relationship that produces ...