论文信息 - On the Relation between Second-Order Statistics, Connectivity Analysis, and Percolation Models in Digital Textures

On the Relation between Second-Order Statistics, Connectivity Analysis, and Percolation Models in Digital Textures

It can be shown that a wide family of digital textures corresponds to percolation models (Pikaz and Averbuch, 1996, Technical Report 315/96, Tel-Aviv University). Percolation models supply statistical results and theoretical background for connectivity analysis. This paper focuses on the relation between second-order statistics (which are very common in use for digital texture analysis) and connectivity analysis. The paper also presents additional evidences to the tight relation between digital textures and percolation models.

Amir Averbuch | Arie Pikaz

[1] S. Redner,et al. Introduction To Percolation Theory , 2018 .

[2] E. Monberg,et al. Percolation and cluster distribution. II. layers, variable-range interactions, and exciton cluster model , 1978 .

[3] Michael Unser,et al. Sum and Difference Histograms for Texture Classification , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4] André Gagalowicz,et al. A New Method for Texture Fields Synthesis: Some Applications to the Study of Human Vision , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5] F. Family,et al. Percolation in an interactive cluster-growth model. , 1988, Physical review. A, General physics.

[6] Amir Averbuch,et al. On automatic threshold selection for polygonal approximations of digital curves , 1996, Pattern Recognit..

[7] Amir Averbuch,et al. Digital image thresholding, based on topological stable-state , 1996, Pattern Recognit..

[8] B. Julesz. Textons, the elements of texture perception, and their interactions , 1981, Nature.

[9] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..