RSF模型的python实现

李老师的rsf模型的python代码Intensity inhomogeneities often occur in real-world images and may cause considerable difficulties in image segmenta- tion. In order to overcome the difficulties caused by intensity inho- mogeneities, we propose a region-based active contour model that draws upon intensity information in local regions at a controllable scale. A data fitting energy is defined in terms of a contour and two fitting functions that locally approximate the image intensities on the two sides of the contour. This energy is then incorporated into a variational level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Due to a kernel function in the data fitting term, in- tensityinformation inlocal regions isextracted toguidethemotion of the contour, which thereby enables our model to cope with in- tensity inhomogeneity. In addition, the regularity of the level set function is intrinsically preserved by the level set regularization term to ensure accurate computation and avoids expensive reini- tialization of the evolving level set function. Experimental results for synthetic and real images show desirable performances of our method.