An Improvement on C-V Model
Minggang Jing, Jitao Wu, Xiaotao Wang · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2013
C-V model has the advantage of being able to detect boundaries of objects that are not defined by gradient.However, when detecting these types of edges, the C-V model only considers the average value of each region without local information.As a result, its segmentation result may exist errors, when detecting non-gradient defined boundaries.In order to overcome this problem, we modify the fitting term of classical C-V model with an extra weight.This weight can control the relative height of zero-level contour, so the new method can decrease the segmentation errors.Experiments show that the new model can obtain more accurate results and segment multi-phase images by setting proper weights.