| Image segmentation is a fundamental problem in the field of image processing and computer vision. Its goal is to partition a given image into several dissimilar parts in each of which the intensity is homogeneous. So far, a wide variety of methods have been proposed to solve the image segmentation problem, in which partial differential equation (PDE) based methods have been proved to be an efficient framework for image segmentation.The infrared image segmentation is widely used in precision weapons, civilian navigation, tracking and other fields. Because of its military and civilian aspects of the application, many of infrared image segmentation methods have been proposed, in which PDE-based methods have not been proved to be an efficient method for infrared image segmentation.This dissertation focuses on PDE-based methods for infrared image segmentation; the main results are summarized as follows:1. A morphological filtering and gradient operator for infrared image segmentation is presented to detect the sea-sky-line in the infrared image (sea-sky-line method).2. All of edge-based PDE methods rely on the edge stopping function. It is typically a decreasing function of the gradient magnitude of Gaussian smoothed image. In identifying object edges, however, image gradient does not take into account some important features such as junctions and corners; this results in the inaccurate location of edges or even false segmentations. Based on a measure of the local coherence of image structure tensor, this paper proposes a new edge stopping function. Experimental results show that a PDE method using the new edge stopping function can significantly perform better in the location of edges, while it is much faster and more robustness to noise than the original method.3. The above sea-sky-line method and PDE method with local coherence are applied to infrared image segmentation. Numerical results show the effectiveness and reliability of the proposed method. |
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