Adaptively active contours based on variable exponent \(L^{p(|\nabla I|)}\) norm for image segmentation
Summary: We propose an \(L^{p(|\nabla I|)}\)-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 \(L^{p(|\nabla I|)}\) (\(p(|\nabla I|) > 2\)) norm instead of the \(L^2\) norm to define the external energy and incorporates an extra internal energy into the overall energy. Due to the variable exponent \(p(|\nabla I|)\) which could fit the image gradient information adaptively, the proposed \(L^{p(|\nabla I|)}\)-based model has the hope of segmenting those images with low contrast and blurred boundaries. Experimental results show that the proposed model with \(p(|\nabla I|) > 2\) really can effectively and quickly segment those images with low contrast and blurred boundaries.
scientific article; zbMATH DE number 6173327