Name
Abstract | ||
|---|---|---|
In this paper, a robust image segmentation method is proposed. The relationship between pixel intensities and distance between
pixels are introduced to the traditional neighbourhood potential function To perform an unsupervised segmentation, the Bayes
Information Criterion (BIC) is used to determine the class number, the K-means is employed to initialise the classification
and calculate the mean values and variances of the classes. The segmentation is transformed to maximize a posteriori(MAP)
procedure. Then, the Iterative Conditional Model (ICM) is employed to solve the MAP problem. In the experiments, the proposed
method is compared with other segmentation techniques, for noisy image segmentation applying on synthetic and real images.
The experiment results shows that the proposed algorithm is the better choice.
|
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11893004_3 | PRICAI |
Keywords | DocType | Volume |
real image,class number,map problem,noisy image segmentation,proposed algorithm,unsupervised segmentation,robust image segmentation method,mrf image segmentation technique,efficient unsupervised mrf image,bayes information criterion,iterative conditional model,segmentation technique,classification ratio | Conference | 4252 |
Issue | ISSN | ISBN |
null | 0302-9743 | 3-540-46537-5 |
Citations | PageRank | References |
1 | 0.40 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yimin Hou | 1 | 5 | 2.91 |
| Lei Guo | 2 | 11 | 5.45 |
| Xiangmin Lun | 3 | 3 | 1.84 |