Parallel scalable libraries and algorithms for computer vision

We describe a project that integrates applications requirements, parallel algorithm design, models of parallel computing, and software tools in order to improve the ability of applications researchers in the fields of computer vision and image processing (CVIP) to realize the performance potential of high performance parallel computers. This objective is achieved by pursuing four directions of research: the development of efficient and practical scalable algorithms for fundamental CVIP problems; the implementation of realistic CVIP scenarios on high performance computers; the development of a scalable, architecture-independent parallel model and a metric for estimating the performance of an algorithm on an existing parallel machine; and the development of prototype software tools. In this paper we describe work in each of these areas