Comparison of the classification methods for the images modeled by Gaussian random fields
In image classification often occur such situations, when images in some level are corrupted by additive noise. Such noise in image classification can be modeled by Gaussian random fields (GRF). In image classification supervised and unsupervised methods are used. In this paper we compare our propos...
Main Authors: | Lijana Stabingienė, Giedrius Stabingis, Kęstutis Dučinskas |
---|---|
Format: | Article |
Language: | English |
Published: |
Vilnius University Press
2011-12-01
|
Series: | Lietuvos Matematikos Rinkinys |
Subjects: | |
Online Access: | https://www.journals.vu.lt/LMR/article/view/15421 |
Similar Items
-
Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model
by: Mei Guo, et al.
Published: (2021-04-01) -
Semi-Supervised Bayesian Classification of Materials with Impact-Echo Signals
by: Jorge Igual, et al.
Published: (2015-05-01) -
Gaussian transformation enhanced semi-supervised learning for sleep stage classification
by: Yifan Guo, et al.
Published: (2023-05-01) -
Comparing the potentials of the different canola flower indices for canola mapping based on Landsat 9 images
by: Haifeng Tian, et al.
Published: (2024-12-01) -
Spatially Weighted Bayesian Classification of Spatio-Temporal Areal Data Based on Gaussian-Hidden Markov Models
by: Kęstutis Dučinskas, et al.
Published: (2023-01-01)