Multiscale Active Contour Methods in Computer Vision with Applications in Tomography

• Main Author: Alvino, Christopher Vincent
• Format: Others
• Language: en_US
• Published: Georgia Institute of Technology 2005
• Subjects: FAS multigrid; Tranmission tomography; Tomographic reconstruction; CT Scan; Surface initialization; Optical flow; Contour registration; Medical imaging; Mumford-Shah; Scale space; Minimal surfaces; Tomography; Computer vision; Diagnostic imaging; Image processing Mathematics
• Online Access: http://hdl.handle.net/1853/6896

Most applications in computer vision suffer from two major difficulties. The first is they are notoriously ridden with sub-optimal local minima. The second is that they typically require high computational cost to be solved robustly. The reason for these two drawbacks is that most problems in computer vision, even when well-defined, typically require finding a solution in a very large high-dimensional space. It is for these two reasons that multiscale methods are particularly well-suited to problems in computer vision. Multiscale methods, by way of looking at the coarse scale nature of a problem before considering the fine scale nature, often have the ability to avoid sub-optimal local minima and obtain a more globally optimal solution. In addition, multiscale methods typically enjoy reduced computational cost. This thesis applies novel multiscale active contour methods to several problems in computer vision, especially in simultaneous segmentation and reconstruction of tomography images. In addition, novel multiscale methods are applied to contour registration using minimal surfaces and to the computation of non-linear rotationally invariant optical flow. Finally, a methodology for fast robust image segmentation is presented that relies on a lower dimensional image basis derived from an image scale space. The specific advantages of using multiscale methods in each of these problems is highlighted in the various simulations throughout the thesis, particularly their ability to avoid sub-optimal local minima and their ability to solve the problems at a lower overall computational cost.