Application of fractal dimension and co-occurrence matrices algorithm in material Vickers hardness image segmentation

Authors:
Wang Guitang;Zhu Jianlin;Cao Peiliang
Affiliations:
Information Engineering Institute, Guangdong University of Technology, Guangzhou, China;Information Engineering Institute, Guangdong University of Technology, Guangzhou, China;College of Life Science, South China Normal University, Guangzhou, China
Venue:
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Year:
2009

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Supervised Textured Image Segmentation Using Feature Smoothing and Probabilistic Relaxation Techniques

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

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.