Adaptively Active Contours Based on Variable Exponent Lp(|∇I|)‎ Norm for Image Segmentation

Abstract EN

We propose an Lp(|∇I|)-based adaptively active contours model for image segmentation which is derived from the well-known Chan-Vese (C-V) model.

Unlike the C-V model, the proposed model uses the Lp(|∇I|) (p(|∇I|)>2) norm instead of the L2 norm to define the external energy and incorporates an extra internal energy into the overall energy.

Due to the variable exponent p(|∇I|) which could fit the image gradient information adaptively, the proposed Lp(|∇I|)-based model has the hope of segmenting those images with low contrast and blurred boundaries.

Experimental results show that the proposed model with p(|∇I|)>2 really can effectively and quickly segment those images with low contrast and blurred boundaries.