The C-V model with motion factor
Jitao Wu · 2010
One advantage of C-V model among the variational level set methods is that it can detect image boundaries which were not defined by gradient.However,when detecting these type boundaries,the C-V model only consider the mean value of each region without local information,so though the C-V model can get non-gradient defined image boundary,its segmentation result contains errors.The above problem is solved by importing the motion factor to the C-V model in this paper.Where,the motion factor is defined as a function of local convexities of image.By adjusting parameters of the motion factor,the novel model can adjust the height of its 0-level set,i.e.,can make the 0-level set get close to the plane which the target belongs to,so can eliminate the partition errors.We present the partial differential model,and experiments validate the quality of the segmentations obtained.