LIP6 2000/021
- Thesis
Représentation de la variabilité dans le traitement d'images flou - A. Rick
- 150 pages - 12/17/1999- document en - http://www.lip6.fr/lip6/reports/2000/lip6.2000.021.pdf - 2,762 Ko
- Contact : rickand (at) nullgemse.fr
- Ancien Thème : APA
- Keywords : Fuzzy Classification, Inductive Learning, Variability, Mixture Models, EM Algorithm, Regression, Prototypes, Markov Models, Mammography, Computer Aided Detection
- Publisher : Valerie.Mangin (at) nulllip6.fr
One of the most important factors, which limit the performance of classifiers in many image processing tasks, is the variability. In this thesis, we will propose a new method to reduce the effect of variability in a fuzzy classification system for image processing by using the context in the image.
The method consists of the following steps:
- Parameterization of the histograms of all the attributes that are calculated on the images for the classification task which gives a compact description with a small number of parameters.
- Conctruction of a prototype which includes the variability and the interactions between the parameters using the learning database.
- Adaptation of the model to the current case by adapting the parameters of the prototype.
The proposed method is applied to a synthetic data base and to a database of mammography images for the detection of dense lesions.
The method consists of the following steps:
- Parameterization of the histograms of all the attributes that are calculated on the images for the classification task which gives a compact description with a small number of parameters.
- Conctruction of a prototype which includes the variability and the interactions between the parameters using the learning database.
- Adaptation of the model to the current case by adapting the parameters of the prototype.
The proposed method is applied to a synthetic data base and to a database of mammography images for the detection of dense lesions.