Name
Abstract | ||
|---|---|---|
In this paper, we present a novel soft decision mixture model for image segmentation. This model adopts the soft decision classify into gaussian mixture model to represent the probability distribution of the observed image feature. The model for the underlying true context images is designed to serve as prior contextual constraints on unobserved pixel labels in term of markov random field model. Experiments with synthetic image and real image show that the use of soft decision mixture model definitely improves the quality of the segmentation results for noisy images and results in reduced classification errors in the interior area of the region. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11539902_107 | ICNC (3) |
Keywords | Field | DocType |
robust soft decision mixture,soft decision mixture model,synthetic image,observed image feature,underlying true context image,gaussian mixture model,markov random field model,real image show,novel soft decision mixture,noisy image,image segmentation,image features,mixture model,probability distribution | Scale-space segmentation,Pattern recognition,Computer science,Markov model,Markov random field,Image processing,Image segmentation,Artificial intelligence,Real image,Mixture theory,Machine learning,Mixture model | Conference |
Volume | ISSN | ISBN |
3612 | 0302-9743 | 3-540-28320-X |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pan Lin | 1 | 1 | 0.69 |
| Feng Zhang | 2 | 1 | 1.03 |
| Chongxun Zheng | 3 | 90 | 16.32 |
| Yong Yang | 4 | 0 | 0.34 |
| Yimin Hou | 5 | 5 | 2.91 |