Image Segmentation Algorithm Based On Active Contours Combined With Improved VFC

Image segmentation is playing a significant role in computer vision processing, and, it is a difficult theme. It aims at dividing an image into several parts according to different features, and then extracts the area of which we interested. The accuracy of the results of segmentation determines the processing followed by to some extent. For that, varieties of methods have been proposed, among which active contour models, or deformable models are playing a popular role. It is outstanding out the other traditional algorithms for the combination of the image information of the lower level and the prior information of the higher level.Image segmentation based on the traditional active contour models, or Snakes which was proposed by Kass in the year of1987is always drawing munch attention of researchers in the filed of image processing. And, it becomes the popular scheme of image segmentation in the filed of computer vision for its solid mathematic foundation and stable numerical solution. It is one of the most popular algorithms which are widely used. The evolution of curves of Snakes model is expressed as a kind of parameter relationships. So, it is a process of curve fitting to some extent. The basic idea in active contour models or snakes is to evolve a curve, subject to constraints from a given image in order to detect objects in that image. It is expected to solve the problems in image segmentation based on traditional methods. However, it has inherent defects in itself, which has great affects on the segmentation results, such as, the sensitivity of the location of original curves, difficulty in disposing the topological changes, and sensitivity to interference factors.Taking those defects represented above into considerations, researchers have proposed all kinds of methods. Among those models, geometric active contour models and its improved ones attract a lot of attention. But, problems always exist, such as, the intensity of original contours and the low speed of evolutions. The application of vector field convolution (VFC) in Snakes provides it a platform in image segmentation. In this paper we combine it with an improved snake model, which is motivated by CV model and VFC, in which a linear transformation is performed on the VFC external force. The combination of VFC and active contour without edges expand the use of VFC in snake models as an external force, which produces sound results during the experiments on image segmentations of synthesis, nature, and medical images. And the results show that it is optimized in both speed and accuracy of the image segmentation process. In this paper, we analyze the traditional image segmentation methods and compared the results of that. We focus on the research of active contour models based on the summary of image segmentation methods. We take the improved VFC as the external force of active, and adjust the parameters to the evolution to expect faster speed, more accurate segmentation results and wide range of use.As is known, it is more convenient to use color images than gray ones in many fields, such as, object organization and tracking. However, the illumination variation is always a key point in the following results of image processing. A new idea to resolve the problem of color image illumination variation is proposed at the end of this thesis. Expected to get sound results of overcoming the illumination variation without changes of colors, which is of great research and application significance.