HierarchicaI Curvature Estimation in Computer Vision

This thcsis cancerns the estimation and description of curvature for computer vision applications. Different types of multi-dimensional data are considered: images (2D)j volumes (3D); time sequences of images (3D); and time sequences of volumes (4D). The methods are based on loeal Fourier domain models and use loeal operations such as fiitering. A hierarchical approach is used. Firstly, the loeal orientation is estimated and represented with a vedor field equivalent description. Secondly, the loeal curvature is estimated from the orientation description. The curvatqre algo-rithms are closely related to the orientation estimation algorithms and the methods as a whole give a unified approach to the estimation and description of orientation and curvature. In addition, the methodology avoids thresholding and premature decision making. Results on both synthetic and real world data are presented to illustrate the algorithms performance with respect to accuracy and noise insensitivity. Examples illustrating the use of the curvature estimates for tasks such as image enhancemen