Application of Fractal Dimension and Co-occurrence Matrices Algorithm in Material Vickers Hardness Image Segmentation
| Autor: | Cao Peiliang, Zhu Jianlin, Wang Guitang |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
business.industry
Segmentation-based object categorization ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION k-means clustering Scale-space segmentation Pattern recognition Image segmentation Fractal dimension Fractal Image texture Computer Science::Computer Vision and Pattern Recognition Vickers hardness test Computer vision Artificial intelligence business Algorithm ComputingMethodologies_COMPUTERGRAPHICS Mathematics |
| Zdroj: | 2009 Third International Symposium on Intelligent Information Technology Application. |
| Popis: | The algorithm of fractal dimension and co-occurrence matrices is proposed and is applied to material Vickers hardness image segmentation. Based on the characteristics of the indentation images, this article uses texture features to extract the indentation silhouette from the point view of texture segmentation. We adopt fractal dimension and co-occurrence matrix algorithm to describe the texture characteristics of the indentation image, forming a n-dimensional feature vector, introducing EPNSQ to smooth the features. Finally we combine with the k-means clustering algorithm to get texture segmentation result. The experiment demonstrates that in the material Vickers hardness image segmentation the proposed algorithm was significantly effective and robust. |
| Databáze: | OpenAIRE |
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