Continuous Potts Model Based SAR Image Segmentation by Using Dictionary-Based Mixture Model
| Autor: | Jilan Feng, Zongjie Cao, Yu Yadan |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Contextual image classification
business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Pattern recognition Probability density function Image segmentation Mixture model Image (mathematics) Computer Science::Computer Vision and Pattern Recognition Artificial intelligence business Energy functional Potts model |
| Zdroj: | The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems ISBN: 9783319005355 |
| DOI: | 10.1007/978-3-319-00536-2_67 |
| Popis: | In this paper, Potts model based on the dictionary-based mixture model (DMM) is proposed to make image classification. Potts model is used for SAR image segmentation by minimizing energy functional, which is a weighted sum of data fidelity and the length of the boundaries of the regions. However, it needs prior information such as the number of regions and the probability density function of image. In this paper, we overcome this problem by using the dictionary-based mixture model, which can compute the optimal number of segments automatically and the probability density function of complex SAR image. Experiments on several real SAR images show that Potts model based on DMM has better performance in SAR image segmentation than that with sole distribution. |
| Databáze: | OpenAIRE |
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