Structurally adaptive mathematical morphology on nonlinear scale-space representations
Résumé
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 regularity, or on size variable neighbourhoods according to the local intensity. This paper introduces a new framework: the structurally adaptive mathematical morphology. More precisely, the rationale behind the present approach is to work on a nonlinear multi-scale image decomposition, and then to adapt intrinsically the size of the operator to the local scale of the structures. The properties of the derived operators are investigated and their practical performances are compared with respect to standard morphological operators using natural image examples.
Mots clés
Adaptive operators
Levelling
Local regularity
Local scale
Morphological operator
Morphological scale-space
Multi-scale image decomposition
Natural images
Nonlinear scale
Structure decomposition
Translation invariants
Adaptive filtering
Adaptive filters
Electric filters
Image processing
Imaging systems
Mathematical operators
Mathematical morphology