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Segmentation d'images couleur par coalescence non supervisée d'histogrammes 2D et fusion de régions selon la théorie de Dempster-Shafer Color image segmentation by unsupervised 2D histogram clustering and Dempster-Shafer region merging
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lézoray, Olivier Charrier, Christophe Ea, Lusac |
| Copyright Year | 2004 |
| Abstract | and key words In this paper, a color image segmentation method based on a new approach called bimarginal is proposed.To overcome the drawbacks of the classical marginal approaches, color components are considered in pairs in order to have a partial view of their inner correlation. Working with color images, the three possible combinations are considered as three independant information sources. Each pairwise component combination is firstly analyzed according to an unsupervised morphologic clustering which looks for the dominant colors of a 2D histogram. This leads to obtain three segmentation maps combined by intersection after being simplified. The intersection process itself producing an over-segmentation of the image, a pairwise region merging is done according to a similarity criterion with the Dempster-Shafer theory up to a termination criterion. To fully automate the segmentation, an energy function is proposed to quantify the segmentation quality. The latter acts as a performance indicator and is used all over the segmentation to tune its parameters. Color, segmentation, clustering, fusion, Dempster-Shafer, multi-scale, quality. traitement du signal 2004_volume 21_numero special L'image numerique couleur 605 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://documents.irevues.inist.fr/bitstream/handle/2042/2437/03%E2%80%A2Lezoray.pdf?sequence=1 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
