Abstract

A systematic approach towards the problem of designing integrated methods for image segmentation has been developed in this thesis. This is aimed towards the analysis of underlying structures in an image which is crucial for a variety of image analysis and computer vision applications. However, a robust identification and measurement of such structure is not always achievable by using a single technique that depends on a single image feature. Thus, it is necessary to make use of various image features, such as gradients, curvatures, homogeneity of intensity values, textures, etc. as well as modelbased information (such as shape). Integration provides a way to make use of the rich information provided by the various information sources, whereby consistent information from the different sources are reinforced while noise and errors are attenuated. As a first step, integration is achieved in this work by using region information in addition to gradient information within the deformable boundary finding framework. This considerably increases the robustness of the final boundary output to noise and initial estimate.

Citations