Name
Title | ||
|---|---|---|
Generalized morphological components based on interval descriptors and n-ary aggregation functions |
Abstract | ||
|---|---|---|
Morphological perceptrons (MPs) can be characterized as feedforward morphological neural networks (MNNs) with applications in classification and regression. The neuronal aggregation functions of current MP versions are drawn from gray-scale mathematical morphology (MM) that can be described in terms of matrix products in a lattice algebra called minimax algebra. Specifically, MPs have components each of which computes a pair-wise infimum of an erosion and an anti-dilation that can be expressed in terms of products of matrices with entries in a complete l-group extension. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.ins.2021.10.012 | Information Sciences |
Keywords | DocType | Volume |
Morphological neural network,Morphological perceptron,Morphological component,Generalized morphological component,Interval descriptor,Lattice-ordered groups,Rings and fields,Bounded l-group,Ordered ring and ordered field extensions | Journal | 583 |
ISSN | Citations | PageRank |
0020-0255 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Sussner | 1 | 880 | 59.25 |
| David Ernesto Caro-Contreras | 2 | 0 | 0.34 |