Segmentation of vegetation scenes: the SIEMS method
| Autor: | Alexandre Alakian |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Unsupervised segmentation Pattern recognition Edge detection Image (mathematics) Geography Expectation–maximization algorithm Metric (mathematics) medicine Computer vision Segmentation Artificial intelligence medicine.symptom Vegetation (pathology) business Image resolution |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.973705 |
| Popis: | This paper presents an unsupervised segmentation method dedicated to vegetation scenes with decametric or metric spatial resolutions. The proposed algorithm, named SIEMS, is based on the iterative use of the Expectation–Maximization algorithm and offers a good trade-off between oversegmentation and undersegmentation. Moreover, the choice of its input parameters is not image–dependent on the contrary to existing technics and its performances are not crucially determined by these input parameters. SIEMS consists in creating a coarse segmentation of the image by applying an edge detection method (typically the Canny–Deriche algorithm) and splitting iteratively the undersegmented areas with the Expectation–Maximization algorithm. The method has been applied on two images and shows satisfactory results. It notably allows to distinguish segments with slight radiometric variations without leading to oversegmentation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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