An Active Contour Model Based on Texture Distribution for Extracting Inhomogeneous Insulators From Aerial Images

The objects in natural images are often texturally inhomogeneous and prone to be falsely segmented into different parts by conventional methods. To overcome the difficulties caused by texture inhomogeneity, a new active contour model is proposed to extract inhomogeneous insulators from aerial images. First, a semilocal operator is employed to extract the texture features of insulators under the Beltrami framework. The layer of semilocal texture feature is single, and thus, it can avoid the high dimensionality of feature space. Then, a new convex energy functional is defined by taking the Xie's nonconvex model into a global minimization active contour framework during the process of segmentation. The proposed energy functional consists of not only the semilocal texture features of insulators but also their spatial relationship, which improves its ability to deal with textural inhomogeneity. Moreover, it can also avoid the existence of local minima in the minimization of the Xie's nonconvex model, thereby being independent of initial contour. In the process of contour evolution and numerical minimization, a fast dual formulation is employed to overcome the drawbacks of the usual level set and gradient descent method and to make the evolution of the contour more efficient. The experimental results on aerial insulator images confirm the ability of the proposed algorithm to effectively segment inhomogeneous textures with an overall average rmse of 1.87 pixels, a precision of 85.59%, and a recall of 86.47%. In addition, the proposed algorithm is extended to animal images, and satisfactory segmentation results can be obtained as well.