Artificial life for image segmentation

Artificial life has been successfully used for understanding biological systems and in many applications in robotics, computer graphics, etc. In this paper, we pioneer the use of artificial life for image segmentation, a challenging area in image processing. Our method associates each pixel in an image with a life and it evolves according to a system of rules. The segmented partitions emerge when the state of the lives reaches an equilibrium. The artificial life approach is promising in image processing because it is inherently parallel and coincides with the self-governing biological process. In addition, it has the advantage of the integration of both detail preservation and noise removal. The experiments demonstrate the feasibility of the artificial life approach on both intensity images and color images. We also compared the approach with other four commonly used methods for three different kinds of noise corrupted images.