From mathematical morphology to machine learning of image operators
DOI10.1007/s40863-022-00303-1OpenAlexW4224231508WikidataQ121767364 ScholiaQ121767364MaRDI QIDQ2082052
Roberto jun. Hirata, Marcelo S. Reis, Junior Barrera, Nina S. T. Hirata, Ronaldo Fumio Hashimoto
Publication date: 30 September 2022
Published in: São Paulo Journal of Mathematical Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s40863-022-00303-1
machine learningmathematical morphologyautomatic designoperator representationoperator decompositionimage operator
Computing methodologies for image processing (68U10) Pattern recognition, speech recognition (68T10) Lattices and duality (06D50) Boolean functions (06E30)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Why mathematical morphology needs complete lattices
- An overview of morphological filtering
- Unification of nonlinear filtering in the context of binary logical calculus. I: Binary filters
- Morphological analysis of discrete random shapes
- Aperture filters.
- From the sup-decomposition to sequential decompositions
- Multiresolution design of aperture operators
- A combinatorial optimization technique for the sequential decomposition of erosions and dilations
- A greedy algorithm for decomposing convex structuring elements
- Optimal Boolean lattice-based algorithms for the U-curve optimization problem
- Nonlinear filter design using envelopes
- U-curve: a branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problem
- Error Bounds for Morphologically Derived Measurements
- Minimal Representations for Translation-Invariant Set Mappings by Mathematical Morphology
- On Dedekind's Problem: The Number of Isotone Boolean Functions. II
- Design of optimal binary filters under joint multiresolution–envelope constraint
- Segmentation of Microarray Images by Mathematical Morphology
- The statistics of natural images
- Solving Problems in Mathematical Morphology through Reductions to the U-Curve Problem
- Reducibility among Combinatorial Problems
- Morphological Networks for Image De-raining
- Optimal mean-square N-observation digital morphological filters
- Multiresolution analysis for optimal binary filters
This page was built for publication: From mathematical morphology to machine learning of image operators