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Topology Study and Numerical Analysis of Active Contour models
active contour models
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In recent years, with the development of computer technology, digital image processing and its applications have become increasingly important. Image segmentation is a vital yet basic type of processing completed before other higher level applications. This thesis explores the topology study and numerical analysis of active contour models applicable to image segmentation. Active contour models can be classified into two types. One is a parametric active contour model and the other is a geometric active contour model. Firstly, a critical look is focuses on the parametric active contour model. In particular, some drawbacks and advantages of this model are discussed. Aiming to alleviate or solve the drawbacks of parametric active contour model, we present some modified models with a different image force field and numerical methods in this dissertation. Next, we discuss and introduce geometric active contour model. Topological flexibility has been long claimed as the major advantage of geometric active contour model over parametric active contour model. Geometric active contour model has a lot of different models. Although all of the geometric active contour models can segment out the outline of image objects, they cannot carry the function of topology control to the outline segmented. The so-called ability of topology control means that handling the “merging or splitting capability” and “capability of selecting objects” in geometric active contour models. We analyze several basic and important models of geometric active contour models and discuss their drawbacks in topology control. Finally, we present the some ideas that are mainly aimed at those drawbacks to modify those different geometric active contour models in this dissertation. We use some images to verify our proposed methods. The segmentation results of the images show that the modified geometric active contour model to provide the ability of topology control for the segment objects.
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