Name
Abstract | ||
|---|---|---|
This paper presents a fully automatic watershed color segmentation scheme which is an extension to color images of a previously reported approach dedicated to segmentation of scalar images. The importance of this extension lies mainly on its ability to automatically select an optimum result out of a hierarchical stack. This achievement is realized through the introduction of new evaluation methods for the segmentation quality of each level of the hierarchy which considers a tradeoff between the preservation of details and the suppression of heterogeneity. The first method estimates the local color error of the regions and combines it with the amount of regions. The second evaluates the contrast of the segmented image by combining a region uniformity with an inter-region contrast measure for all regions. These two methods are compared with respect to an existing one. Experimental results demonstrate the improvement which has been achieved by using the new evaluation criteria. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/0-306-47025-X_23 | Computational Imaging and Vision |
Keywords | Field | DocType |
hierarchical watershed segmentation,color,evaluation criteria | Scale-space segmentation,Pattern recognition,Computer science,Local color,Segmentation,Scalar (physics),Image segmentation,Real-time computing,Watershed,Artificial intelligence,Hierarchy | Conference |
Volume | Citations | PageRank |
18 | 4 | 0.48 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iris Vanhamel | 1 | 100 | 9.96 |
| H. Sahli | 2 | 4 | 0.48 |
| I. Pratikakis | 3 | 809 | 36.03 |