Multiscale Methods For The Segmentation Of Images

This work presents a method for segmenting images based on gradients in the intensity function. Past approaches have centered on formulating the problem in the context of variational calculus as the minimization of a functional involving the image intensity and edge functions. Computational methods for finding the minima of such variational problems are prone to two shortfalls: they are often computationally intensive and almost always incapable of computing error statistics associated with the segmentation. Using a particular variational formulation as a starting point, this paper presents a derivation of an associated statistical formulation using multiscale models. The result is an algorithm which is fast and capable of computing error statistics. 1. INTRODUCTION Many applications require the segmentation of images. In disciplines such as medical imaging and remote sensing, this is often done painstakingly by hand. To reduce the tedium of this component of image analysis, one woul..