Kuosmanen et al., 1995 - Google Patents

Shape preservation criteria and optimal soft morphological filtering

Kuosmanen et al., 1995

Document ID
2239443406509558475
Author
Kuosmanen P
Koivisto P
Huttunen H
Astola J
Publication year
Publication venue
Journal of Mathematical Imaging and Vision

External Links

Snippet

New criteria for shape preservation are presented. These criteria are applied in optimizing soft morphological filters. The filters are optimized by simulated annealing and genetic algorithms which are briefly reviewed. Situations, where this kind of criteria give better …
Continue reading at link.springer.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models

Similar Documents

Publication Publication Date Title
Bouboulis et al. Adaptive learning in complex reproducing kernel Hilbert spaces employing Wirtinger's subgradients
Kuosmanen et al. Shape preservation criteria and optimal soft morphological filtering
Zhou et al. Online filter clustering and pruning for efficient convnets
Yan Prototype optimization for nearest neighbor classifiers using a two-layer perceptron
CN114821058A (en) Image semantic segmentation method and device, electronic equipment and storage medium
CN104992407B (en) A kind of image super-resolution method
Majid et al. Impulse noise filtering based on noise-free pixels using genetic programming
Qian et al. On the use of Gibbs Markov chain models in the analysis of images based on second-order pairwise interactive distributions
Huttunen et al. Optimization of soft-morphological filters by genetic algorithms
CN105046712A (en) Adaptive Gauss differential evolution based circle detection method
Castillo et al. The Little–Hopfield model on a sparse random graph
Yang et al. Extracting Boolean rules from CA patterns
Hinton et al. An unsupervised learning procedure that discovers surfaces in random-dot stereograms
CN114581470B (en) Image edge detection method based on plant community behaviors
Nakajima et al. Riddled basins of the optimal states in learning dynamical systems
CN113382126B (en) Image reversible information hiding method and system based on attention guidance
CN104021563B (en) Method for segmenting noise image based on multi-objective fuzzy clustering and opposing learning
Koivisto et al. Training-based optimization of soft morphological filters
Astola et al. Nonlinear filters
Kuosmanen et al. Optimal soft-morphological filtering under shape preservation criteria
CN112132181B (en) Image true and false identification method based on generation type countermeasure network
Haseyama et al. A genetic algorithm based image segmentation for image analysis
Craiu et al. Pattern generation using likelihood inference for cellular automata
Derin The use of Gibbs distributions in image processing
Brizzotti et al. The influence of clustering techniques in the RBF networks generalization