New Fractal Model of Grid Overlapping Differential Box-counting
一种新的网格重叠差分盒分形模型

One of the most important feature of the fractal dimension is that its fractal dimension is largely correlated with the surface roughness of image feeled by the human eye. The fractal has many successful applications in analysis,segmentation and classification of images, the differential box-account method is often used in the fractal dimension estimation based on fractal theory. Least squares is better for detailed textures, but is not satisfactory for more coarse textures, in order to solve this problem, overlapping grid differential box-account model was presented, the grids which the numbers of the boxes arc calculated by arc overlapped, the numbers of boxes of the whole image arc calculated on the statistical self-similarity. Linear fit of least square of this model is better,in order to improve the precision of box-counting,non-integer box-counting and zoom-scale-fixing were presented,the fractal dimension of the image texture got by the model was validated, compared with differential box-account.