Variational Problems and Level Set Methods in Computer Vision - Theory and Applications
Abstract: Current state of the art suggests the use of variational formulations for solving a variety of computer vision problems. This thesis deals with such variational problems which often include the optimization of curves and surfaces. The level set method is used throughout the work, both as a tool in the theoretical analysis and for constructing practical algorithms. One frequently occurring example is the problem of recovering three-dimensional (3D) models of a scene given only a sequence of images. Other applications such as segmentation are also considered. The thesis consists of three parts. The first part contains a review of background material and the level set method. The second part contains a collection of theoretical contributions such as a gradient descent framework and an analysis of several variational curve and surface problems. The third part contains contributions for applications such as a framework for open surfaces and variational surface fitting to different types of data.
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