Cloud computing has become a common technology for provisioning infrastructure services on-demand. Modern Cloud platforms can provide cloud-based applications, software, deployment platforms, or general infrastructure services that may include both computational and storage resources. However existing Cloud provisioning models are based on proprietary solutions and don't allow the combination of services from different providers and/or user legacy application that usually are present in user home organizations or campus networks. This paper introduces GEM Bus (GEANT Multi-domain Bus), a service-oriented middleware platform that allows flexible services composition, and their on-demand provisioning and deployment to create new specialized task-oriented services and applications. GEM Bus is built upon state-of-the-art Enterprise Service Bus (ESB) technologies and extend them with new functionalities that allow dynamic component services deployment, composition and management. The current paper discusses the general case for integration of Service-Oriented Architecture (SOA) principles and technologies with the provision and deployment mechanisms of Cloud-based platforms to support on-demand infrastructure services provisioning. It describes the Composable Services Architecture (CSA) that provides a general framework for GEM Bus services design and operation. The paper also presents the current GEM Bus implementation status and discusses how it can be applied as a general SOA platform for Cloud-based service provisioning. Finally, it discusses the practical use case of the federated network monitoring service that can be used as integration component in creating/building GEM Bus based Cloud infrastructure services.

The concepts and design issues that provide the basis for the I/sup 2/F (image interpretation foundations) system are described. The I/sup 2/F system combines object-oriented design for machine vision software and constraint-based geometric modeling into a flexible and effective system for automatic template-guided visual inspection. Object-oriented design for 2-D geometry-based image analysis is discussed, and results from processing experimental X-ray data are presented.>

Projectively invariant shape descriptors efficiently identify instances of object models in images without reference to object pose. These descriptions rely on frame independent representations of planar curves, using plane conies. We show that object pose can be determined from coplanar curves, given such a frame independent representation. This result is demonstrated for real image data. The shape of objects in images changes as the camera is moved around. This extremely simple observation represents the dominant problem in model based vision. Nielsen [4, 5] first suggested using projectively invariant labels as landmarks for navigation. Recent papers [1, 2] have shown that it is possible to compute shape descriptors of arbitrary plane objects that are unaffected by

The authors show how a maximum likelihood estimator can be constructed for homogeneous texture. The estimator turns out to be strikingly simple. It simply enforces, iteratively, coincidence of the centroid of the observed elements with the centroid of the imaging window. Whereas the estimator is based on first moments, error bounds are obtained, the authors show, from second moments. Experiments indicate that, for real textures, orientation estimates fall well inside predicted bounds.>