A Fast Region-Based Segmentation Model with Gaussian Kernel of Fractional Order

By summarizing some classical active contour models from the view of level set representation, a simple energy function expression with the Gaussian kernel of fractional order is proposed, and then a novel region-based geometric active contour model is established. In this proposed model, the energy function with value of [？1,？1] is built, the local mean and global mean of the inside and outside of the evolution curve are employed, and the segmentation results are obtained by controlling the expansion and contraction of the evolution curve. The model is simple and easy to implement; it can also protect weak edges because of considering more statistical information. Experimental results on synthetic and natural images show that the proposed model is much more effective in dealing with the images with weak or blurred edges, and it takes less time. 1. Introduction Image segmentation is a basic and important topic in the fields of image processing. Accurate image segmentation can provide more important information for the follow-up application, such as machine vision and motion tracking. However, segmental results are always affected by low contrast and the problems of intensity inhomogeneity. The main idea of image segmentation is to extract the concerned regions and their contours from the whole image. There have been thousands of image segmentation algorithms proposed in recent decades. Some researchers put forward the edge detection based on the gradient, derivatives, or Canny edge detection, and so on. Edge detection is good for simple image but not suitable for the clutter target boundary extraction. The main reasons are as follows. Firstly, edge extracted for complex image is often not corresponding to the target boundary. Secondly, the extracted edge is discontinuous, but the goal often needs closed boundary to separate the object from the whole image. In addition, edge detection is dependent on the local information near pixel; it has advantages sometimes, but in many cases overall appearance of the target is the key, so the concepts of the image segmentation and edge detection are not one and the same. Regional growth is a simple technique to provide segmental region; the algorithm begins with some seed points and found pixels near the seed which has similar image characteristics, such as gray scale and color characteristics. This algorithm has been applied to Mumford-Shah function [1]. Another region-based method is active contour (AC) model [2]. Active contour model is 2D or 3D surface contour description, which involves the contour evolution