Name
Abstract | ||
|---|---|---|
The image segmentation is very sensitive to the features used in the similarity measure and the objects type. In this paper we introduce a new segmentation algorithm based on fuzzy clustering. This method allows to incorporate spatial information which yield the result more accurate and more robust to noise. It is completely automatized with respect to the number of clusters and the setting up of membership functions. The data structure based on a Fuzzy Tree Algorithm allows to reduce the CPU time. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.fss.2006.10.007 | Fuzzy Sets and Systems |
Keywords | Field | DocType |
image color segmentation,new segmentation algorithm,fuzzy decision tree,spatial information,fuzzy image segmentation,data structure,objects type,fuzzy clustering,fuzzy tree algorithm,cpu time,membership function,graph,similarity measure,image segmentation | Fuzzy clustering,Scale-space segmentation,Segmentation-based object categorization,Image segmentation,Fuzzy set,Tree structure,Artificial intelligence,Minimum spanning tree-based segmentation,Pattern recognition,Fuzzy logic,Algorithm,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
158 | 3 | Fuzzy Sets and Systems |
Citations | PageRank | References |
6 | 0.53 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Doncescu, A. | 1 | 86 | 25.70 |
| Joseph Aguilar-Martin | 2 | 56 | 10.96 |
| Jean-charles Atine | 3 | 7 | 0.88 |