Automatic Selection of Stochastic Watershed Hierarchies
| Autor: | Amin Fehri, Santiago Velasco-Forero, Fernand Meyer |
|---|---|
| Přispěvatelé: | Centre de Morphologie Mathématique (CMM), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Watershed Computer science Stochastic Watershed Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Mathematical morphology 01 natural sciences Machine Learning (cs.LG) Segmentation [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 010306 general physics Hierarchies Hierarchy business.industry Pattern recognition Image segmentation Computer Science - Learning Computer Science::Computer Vision and Pattern Recognition [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] 020201 artificial intelligence & image processing Artificial intelligence Mathematical Morphology business |
| Zdroj: | European Conference of Signal Processing (EUSIPCO) European Conference of Signal Processing (EUSIPCO), 2016, Budapest, Hungary EUSIPCO |
| Popis: | The segmentation, seen as the association of a partition with an image, is a difficult task. It can be decomposed in two steps: at first, a family of contours associated with a series of nested partitions (or hierarchy) is created and organized, then pertinent contours are extracted. A coarser partition is obtained by merging adjacent regions of a finer partition. The strength of a contour is then measured by the level of the hierarchy for which its two adjacent regions merge. We present an automatic segmentation strategy using a wide range of stochastic watershed hierarchies. For a given set of homogeneous images, our approach selects automatically the best hierarchy and cut level to perform image simplification given an evaluation score. Experimental results illustrate the advantages of our approach on several real-life images datasets. in European Conference of Signal Processing (EUSIPCO), 2016, Budapest, Hungary |
| Databáze: | OpenAIRE |
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