Fast and Robust Active Contours Model for Image Segmentation
Image segmentation using local region-based active contour models can segment images with intensity inhomogeneity effectively, but their segmentation results are sensitive to the initialization and easy to get incorrect results when dealing with texture images. This paper presents a novel active contour model (ACM) for image segmentation. The proposed method adopts local kernel mapping to enhance the discriminative ability to delineate nonlinear decision boundaries between classes. In addition, we introduce a modified convex model and propose a fast evolving scheme accordingly to deal with the minimization of the model energy function. The proposed approach is validated by a comparative study over a large number of experiments on synthetic and real images. The experiments demonstrate that our method is more efficient and robust for segmenting different kinds of images compared with the state-of-the-art image segmentation methods.
This paper is concerned with stochastic stability of semi-Markov positive systems with actuator saturation. Firstly, the convex hull technique is employed to deal with actuator saturation. Then, through multiple linear co-positive Lyapunov function, sufficient conditions of stochastic stability for semi-Markov positive systems are derived. Finally, an example is given to demonstrate the validity of the main results.