Fast and active texture segmentation based on orientation and local variance

This paper describes a fast and active texture segmentation approach based on the orientation and the local variance. First, a set of feature images are extracted using the orientation and the local variance. To reduce the computational complexity, a separability measurement method, which is used for selecting four feature images with good separability in four orientations, is proposed in this paper. To improve the segmentation, we adopt a nonlinear diffusion filtering to smooth the four feature images. Finally, a variational framework incorporating these features in a level set based, unsupervised segmentation process is adopted. To improve the computational speed, instead of solving the Euler–Lagrange equation, we calculate the energy, with level set representation, to solve the variational framework. Segmentation results of various synthetic and real textured images has demonstrated that our method has good performance and efficiency.