Name
Abstract | ||
|---|---|---|
Stochastic modeling in image analysis aims to represent the images features in a small number of parameters so as to recognize the source producing the images. In this paper we address the image segmentation problem in the case of significantly differ segments' sizes. A probabilistic model dealing the distribution of gray level in the observed image is based on the Gaussian Mixture Model identifying each component a segment. According to the general segmentation methodology for multi-modal gray levels images we presume that every region-of-interest attaches to a distinct substantial mode of the empirical distribution of gray levels. So, the number of the components is evaluated via a new resampling procedure involving the Expectation-Maximization algorithm used in order to estimate the significant histograms picks. Stable states of our model are associated within of the proposed method with the \"true\" segments quantities specified by the appropriate components' quantities. Numerical experiments demonstrate the high ability of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ARES.2014.55 | Availability, Reliability and Security |
Keywords | Field | DocType |
Gaussian processes,expectation-maximisation algorithm,feature extraction,image recognition,image representation,image sampling,image segmentation,medical image processing,mixture models,stochastic processes,Gaussian mixture model,expectation-maximization algorithm,histogram estimation,image analysis,image feature representation,medical image segmentation,multimodal gray level empirical distribution,probabilistic model,resampling procedure,source recognition,stochastic model,EM clustering,mage segmentation,stochastic model | Scale-space segmentation,Feature detection (computer vision),Pattern recognition,Image texture,Computer science,Segmentation-based object categorization,Image segmentation,Region growing,Artificial intelligence,Mixture model,Minimum spanning tree-based segmentation | Conference |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Z. Barzily | 1 | 35 | 3.97 |
| M. Ding | 2 | 34 | 2.73 |
| Z. Volkovich | 3 | 74 | 13.19 |