16 results on '"Lijana Stabingienė"'
Search Results
2. Application of spatial classification rules for remotely sensed images
- Author
-
Giedrius Stabingis and Lijana Stabingienė
- Subjects
image classification ,spatial classification rules ,supervised classification ,Mathematics ,QA1-939 - Abstract
In this paper the remote sensed image classification example using spacial classification rule with distance (SCRD) is examined. This supervised classification method was first presented in paper [11]. This method is improved version of earlier method PBDF [4, 10, 9], during the classification it incorporates more spatial information. The advantage of this method is its ability to classify data which is corrupted by Gaussian random field and it is typical to remotely sensed images classified in this letter which are corrupted by clouds. Classification accuracy is compared with earlier method and with other commonly used supervised classification methods.
- Published
- 2014
- Full Text
- View/download PDF
3. Comparison of spatial classification rules with different conditional distributions of class label
- Author
-
Giedrius Stabingis, Kęstutis Dučinskas, and Lijana Stabingienė
- Subjects
Bayes discriminant functions ,supervised classification ,spatial dependency ,Analysis ,QA299.6-433 - Abstract
In this paper spatial classification rules based on Bayes discriminant functions are considered. The novelty of this work is that the statistical supervised classification method is improved by extending the influence of spatial correlation between observation to be classified and training sample. Such methods are used for data containing spatially correlated noise. Method accuracy is tested experimentally on artificially corrupted images. This classification rule with distance based conditional distribution for class label shows advantage against other classification rule ignoring such influence and against other commonly used supervised classification methods.
- Published
- 2014
- Full Text
- View/download PDF
4. Classification of the real remotely sensed image covered with clouds
- Author
-
Lijana Stabingienė
- Subjects
image classification ,Gaussian random fields ,Bayes discriminant function ,supervised classification ,semivariance ,Mathematics ,QA1-939 - Abstract
In this paper supervised classification method is proposed. It is based on Bayes discriminant functions (BDF) and it deals with the problem of optimal classification for images, which are corrupted by natural phenomenon such as cloud, smoke or fog. Solving such a problem is very important when we have remotely sensed information, which very often is corrupted by clouds. For example, the remotely sensed images from the territory of Lithuania are very often corrupted by clouds. The idea of classification, using BDF with incorporated spatial dependency between the observation to be classified and the training sample is presented in earlier works of the author. The novelty of this paper is the method how to use these methods for the real situation, i.e. for the remotely sensed image which is naturally covered by clouds. Visual and numerical results are presented in this paper, which show the advantage of this method against BDF ignoring spatial dependency between training sample and observation to be classified and against the method using grey level cooccurrence matrices.
- Published
- 2012
- Full Text
- View/download PDF
5. Comparison of the classification methods for the images modeled by Gaussian random fields
- Author
-
Lijana Stabingienė, Giedrius Stabingis, and Kęstutis Dučinskas
- Subjects
image classification ,Gaussian random fields ,supervised classification ,Bayes discriminant function ,unsupervised classification ,grey level co-occurrence matrix ,Mathematics ,QA1-939 - Abstract
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 proposed supervised classification methods based on plugin Bayes discriminant functions (PBDF) (see [6] and [11]) with unsupervised classification method based on grey level co-occurrence matrix (GLCM) (see e.g. [8] and [1]). The remotely sensed image is used for classification (USGS Earth Explorer). Also GRF with different spatial correlation range are generated and added to the original remotely sensed image. Such situation can naturally occur during forest fire, when smoke covers some territory. These images are used for classification accuracy examination.
- Published
- 2011
- Full Text
- View/download PDF
6. Comparison of linear discriminant functions in image classification
- Author
-
Lijana Stabingienė, Giedrius Stabingis, and Kęstutis Dučinskas
- Subjects
training sample ,Markov Random Fields ,spatial correlation ,Mathematics ,QA1-939 - Abstract
In statistical image classification it is usually assumed that feature observations given labels are independently distributed. We have retracted this assumption by proposing stationary Gaussian random field (GRF) model for features observations. Conditional distribution of label of observation to be classified is assumed to be dependent on its spatial adjacency with training sample spatial framework. Perfomance of the Bayes discriminant function (BDF) and performance of plug-in BDF are tested and are compared with ones ignoring spatial correlation among feature observations.For illustration image of figure corrupted by additive GRF is analyzed. Advantage of proposed BDF against competing ones is shown visually and numerically.
- Published
- 2010
- Full Text
- View/download PDF
7. Error rates in spatial classification of Gaussian data with random labeling
- Author
-
Lijana Stabingienė and Kęstutis Dučinskas
- Subjects
supervised classification ,Gaussian Random Fields ,spatial correlation ,Mathematics ,QA1-939 - Abstract
In spatial classification it is usually assumed that features observations given labels are independently distributed. We have retracted this assumption by proposing stationary Gaussian random field model for features observations. The label are assumed to follow Disrete Random Field (DRF) model. Formula for exact error rate based on Bayes discriminant function (BDF) is derived. In the case of partial parametric uncertainty (mean parameters and variance are unknown), the approximation of the expected error rate associated with plug-in BDF is also derived. The dependence of considered error rates on the values of range and clustering parameters is investigated numerically for training locations being second-order neighbors to location of observation to be classified.
- Published
- 2010
- Full Text
- View/download PDF
8. Adaptive Eye Fundus Vessel Classification for Automatic Artery and Vein Diameter Ratio Evaluation
- Author
-
Lijana Stabingienė, Povilas Treigys, Alvydas Paunksnis, Ramutė Vaičaitienė, Giedrius Stabingis, Gintautas Dzemyda, and Jolita Bernatavičienė
- Subjects
eye fundus images ,business.industry ,Applied Mathematics ,automatic vessel classification ,vessel measurement ,artery-vein ratio ,Anatomy ,03 medical and health sciences ,Diameter ratio ,0302 clinical medicine ,medicine.anatomical_structure ,cardiovascular system ,medicine ,Vein ,business ,030215 immunology ,Information Systems ,Artery - Abstract
Eye fundus imaging is a useful, non-invasive tool in disease progress tracking, in early detection of disease and other cases. Often, the disease diagnosis is made by an ophthalmologist and automatic analysis systems are used only for support. There are several commonly used features for disease detection, one of them is the artery and vein ratio measured according to the width of the main vessels. Arteries must be separated from veins automatically in order to calculate the ratio, therefore, vessel classification is a vital step. For most analysis methods high quality images are required for correct classification. This paper presents an adaptive algorithm for vessel measurements without the necessity to tune the algorithm for concrete imaging equipment or a specific situation. The main novelty of the proposed method is the extraction of blood vessel features based on vessel width measurement algorithm and vessel spatial dependency. Vessel classification accuracy rates of 0.855 and 0.859 are obtained on publicly available eye fundus image databases used for comparison with another state of the art algorithms for vessel classification in order to evaluate artery-vein ratio (AV R). The method is also evaluated with images that represent artery and vein size changes before and after physical load. Optomed OY digital mobile eye fundus camera SmartscopeM5 PRO is used for image gathering.
- Published
- 2018
9. Comparison of spatial classification rules with different conditional distributions of class label
- Author
-
Kęstutis Dučinskas, Lijana Stabingienė, and Giedrius Stabingis
- Subjects
supervised classification ,Spatial correlation ,Bayes discriminant functions ,Computer science ,business.industry ,Applied Mathematics ,lcsh:QA299.6-433 ,spatial dependency ,Pattern recognition ,Sample (statistics) ,lcsh:Analysis ,Conditional probability distribution ,Class (biology) ,Bayes' theorem ,ComputingMethodologies_PATTERNRECOGNITION ,Discriminant ,Classification rule ,Artificial intelligence ,Noise (video) ,supervised classification ,business ,Analysis - Abstract
In this paper spatial classification rules based on Bayes discriminant functions are considered. The novelty of this work is that the statistical supervised classification method is improved by extending the influence of spatial correlation between observation to be classified and training sample. Such methods are used for data containing spatially correlated noise. Method accuracy is tested experimentally on artificially corrupted images. This classification rule with distance based conditional distribution for class label shows advantage against other classification rule ignoring such influence and against other commonly used supervised classification methods.
- Published
- 2014
10. Expected Bayes Error Rate in Supervised Classification of Spatial Gaussian Data
- Author
-
Kęstutis Dučinskas and Lijana Stabingienė
- Subjects
Random field ,Covariance function ,business.industry ,Applied Mathematics ,Statistical parameter ,Pattern recognition ,Conditional probability distribution ,Gaussian random field ,Statistical classification ,Conditional independence ,Bayes error rate ,Artificial intelligence ,business ,Algorithm ,Information Systems ,Mathematics - Abstract
In the usual statistical approach of spatial classification, it is assumed that the feature observations are independent conditionally on class labels (conditional independence). Discarding this popular assumption, we consider the problem of statistical classification by using multivariate stationary Gaussian Random Field (GRF) for modeling the conditional distribution given class labels of feature observations. The classes are specified by multivariate regression model for means and by common factorized covariance function. In the two-class case and for the class labels modeled by Random Field (RF) based on 01 divergence, the formula of the Expected Bayes Error Rate (EBER) is derived. The effect of training sample size on the EBER and the influence of statistical parameters to the values of EBER are numerically evaluated in the case when the spatial framework of data is the subset of the 2-dimensional rectangular lattice with unit spacing.
- Published
- 2011
11. Kaimynystės schemos parinkimas klasifikuojant SCRD metodu
- Author
-
Giedrius Stabingis and Lijana Stabingienė
- Subjects
Scheme (programming language) ,supervised classification ,neighborhood schemes ,spatial classification ,neighborhood ,Computer science ,lcsh:Mathematics ,lcsh:QA1-939 ,computer.software_genre ,Data mining ,computer ,Selection (genetic algorithm) ,computer.programming_language - Abstract
When using Spatial Correlation Rule with Distance (SCRD) the selection of the neighborhood scheme influences classification accuracy. Spatial dependency in different situations remains at various distances, so, according to this, in applications it is important to choose a suitable neighborhood scheme. In the earlier papers of the authors, the nearest neighbor scheme was used. In this paper, several different neighborhood schemes are examined by large experiment.
- Published
- 2015
12. Application of spatial classification rules for remotely sensed images
- Author
-
Lijana Stabingienė and Giedrius Stabingis
- Subjects
supervised classification ,Contextual image classification ,business.industry ,Computer science ,lcsh:Mathematics ,Pattern recognition ,spatial classification rules ,lcsh:QA1-939 ,Gaussian random field ,ComputingMethodologies_PATTERNRECOGNITION ,Classification rule ,Classification methods ,Spatial classification ,Artificial intelligence ,business ,Spatial analysis ,image classification - Abstract
In this paper the remote sensed image classification example using spacial classification rule with distance (SCRD) is examined. This supervised classification method was first presented in paper [11]. This method is improved version of earlier method PBDF [4, 10, 9], during the classification it incorporates more spatial information. The advantage of this method is its ability to classify data which is corrupted by Gaussian random field and it is typical to remotely sensed images classified in this letter which are corrupted by clouds. Classification accuracy is compared with earlier method and with other commonly used supervised classification methods.
- Published
- 2014
13. Klasifikavimas realaus nuotolinio stebėjimo vaizdo, padengto debesimis
- Author
-
Lijana Stabingienė
- Subjects
supervised classification ,Computer science ,lcsh:Mathematics ,image classification ,Gaussian random fields ,Bayes discriminant function ,semivariance ,lcsh:QA1-939 ,Remote sensing ,Image (mathematics) - Abstract
In this paper supervised classification method is proposed. It is based on Bayes discriminant functions (BDF) and it deals with the problem of optimal classification for images, which are corrupted by natural phenomenon such as cloud, smoke or fog. Solving such a problem is very important when we have remotely sensed information, which very often is corrupted by clouds. For example, the remotely sensed images from the territory of Lithuania are very often corrupted by clouds. The idea of classification, using BDF with incorporated spatial dependency between the observation to be classified and the training sample is presented in earlier works of the author. The novelty of this paper is the method how to use these methods for the real situation, i.e. for the remotely sensed image which is naturally covered by clouds. Visual and numerical results are presented in this paper, which show the advantage of this method against BDF ignoring spatial dependency between training sample and observation to be classified and against the method using grey level cooccurrence matrices.
- Published
- 2012
14. Comparison of the classification methods for the images modeled by Gaussian random fields
- Author
-
Giedrius Stabingis, Lijana Stabingienė, and Kęstutis Dučinskas
- Subjects
Spatial correlation ,supervised classification ,Random field ,Contextual image classification ,Computer science ,business.industry ,unsupervised classification ,Gaussian ,lcsh:Mathematics ,grey level co-occurrence matrix ,Pattern recognition ,Gaussian random fields ,lcsh:QA1-939 ,Range (mathematics) ,symbols.namesake ,Bayes' theorem ,Discriminant ,symbols ,Artificial intelligence ,Noise (video) ,business ,Bayes discriminant function ,image classification - Abstract
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 proposed supervised classification methods based on plugin Bayes discriminant functions (PBDF) (see [6] and [11]) with unsupervised classification method based on grey level co-occurrence matrix (GLCM) (see e.g. [8] and [1]). The remotely sensed image is used for classification (USGS Earth Explorer). Also GRF with different spatial correlation range are generated and added to the original remotely sensed image. Such situation can naturally occur during forest fire, when smoke covers some territory. These images are used for classification accuracy examination.
- Published
- 2011
15. Tiesinių diskriminantinių funkcijų taikymas vaizdų atpažinime
- Author
-
Lijana Stabingienė, Giedrius Stabingis, and Kęstutis Dučinskas
- Subjects
Spatial correlation ,Contextual image classification ,business.industry ,lcsh:Mathematics ,Pattern recognition ,Conditional probability distribution ,Linear discriminant analysis ,lcsh:QA1-939 ,training sample ,Markov Random Fields ,spatial correlation ,Gaussian random field ,Bayes' theorem ,Discriminant function analysis ,Feature (computer vision) ,Artificial intelligence ,business ,Mathematics - Abstract
In statistical image classification it is usually assumed that feature observations given labels are independently distributed. We have retracted this assumption by proposing stationary Gaussian random field (GRF) model for features observations. Conditional distribution of label of observation to be classified is assumed to be dependent on its spatial adjacency with training sample spatial framework. Perfomance of the Bayes discriminant function (BDF) and performance of plug-in BDFare tested and are compared with ones ignoring spatial correlation among feature observations.For illustration image of figure corrupted by additive GRF is analyzed. Advantage of proposed BDF against competing ones is shown visually and numerically.
- Published
- 2010
16. Error rates in spatial classification of Gaussian data with random labeling
- Author
-
Kęstutis Dučinskas and Lijana Stabingienė
- Subjects
supervised classification ,business.industry ,Gaussian ,lcsh:Mathematics ,Pattern recognition ,lcsh:QA1-939 ,Gaussian Random Fields ,symbols.namesake ,spatial correlation ,symbols ,Spatial classification ,Artificial intelligence ,business ,Mathematics - Abstract
In spatial classification it is usually assumed that features observations given labels are independently distributed. We have retracted this assumption by proposing stationary Gaussian random field model for features observations. The label are assumed to follow Disrete Random Field (DRF) model. Formula for exact error rate based on Bayes discriminant function (BDF) is derived. In the case of partial parametric uncertainty (mean parameters and variance are unknown), the approximation of the expected error rate associated with plug-in BDF is also derived. The dependence of considered error rates on the values of range and clustering parameters is investigated numerically for training locations being second-order neighbors to location of observation to be classified.
- Published
- 2010
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