On Image Segmentation Based on Local Entropy Fitting Under Nonconvex Regularization Term Constraints

Abstract

The energy functional of the CV and LBF model is single, which makes the curve to get into the local minimum easily during the evolution process, and results inaccurate segmentation of the images with nonuniform grayscale and nonsmooth edges. The proposed algorithm, which is based on local entropy fitting under the constraint of nonconvex regularization term, is used to deal with such problems. In this algorithm, global information and local entropy are fitted to avoid segmentation falling into local optimum, and nonconvex regularization term is imported for constraint to protect edge smoothing. First, global information is used to evolve the approximate contour curve of the target segmentation. Then, a local energy functional with local entropy information is constructed to avoid the segmentation process from falling into a local minimum, and to precisely segment the image. Finally, nonconvex regularization terms are used in the energy functional to protect the smoothness of edge information during image segmentation process. The experimental results clearly indicate that the new algorithm can effectively resist noise, precisely segment images with nonuniform grayscale, and achieve the global optimal.

A Novel Feature Extraction Algorithm and System for Flexible Integrated Circuit Packaging Substrate

“…The basic idea of the energy functional segmentation algorithm [ 18 , 19 , 20 , 21 , 22 ] is to transform the image segmentation problem into a mathematical problem of solving the minimum value of the function and achieve the purpose of image segmentation by controlling the evolution of the curve to the boundary. When the energy functional is a convex function, when the gradient descent method is used to solve the minimum value of the function, the unique value obtained is different from the initial condition.…”