Preserving Information from Real Objects to Digital Shapes

Abstract

Nowadays, the success of the scientific enterprise largely depends on the ability of sharing resources among the scientific community. This problem is particularly relevant in the field of Computer Graphics and Vision. During the last years specific domains ontologies are emerging in support of different contexts, aiming at facilitating automated resource-sharing among information systems in specific fields. A fast evolution of Computer Graphics and Vision is now conditioned by how research teams will be able to intercommunicate. The shared resources should preserve as much meaningful information as possible, in order to allow and improve collaborative research and complete understanding of complex tasks. A critical phase in Computer Graphics and Vision is the Acquisition Phase, which includes different conditions and properties related to the object to be scanned, to the surrounding environment or even to the knowledge of the scanning experts. The novelty of our work is the tentative to integrate Knowledge Management approaches to Computer Graphics and Vision and, in particular, we aim at preserving information when passing from the real world (Real Objects) to the digital one (Digital Shapes). This is a fundamental step for moving knowledge from humans to machines.