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Abstract
Visualizing quantitative time-dependent changes in the topography requires relying on a series of discrete given multi-temporal topographic datasets that were acquired on a given time-line. The reality of physical phenomenon occurring during the acquisition times is complex when trying to mutually model the datasets; thus, different levels of spatial inter-relations and geometric inconsistencies among the datasets exist. Any straight forward simulation will result in a truncated, ill-correct and un-smooth visualization. A desired quantitative and qualitative modelling is presumed to describe morphologic changes that occurred, so it can be utilized to carry out more precise and true-to-nature visualization tasks, while trying to best describe the reality transition as it occurred. This research paper suggests adopting a fully automatic hierarchical modelling mechanism, hence implementing several levels of spatial correspondence between the topographic datasets. This quantification is then utilized for the datasets morphing and blending tasks required for intermediate scene visualization. The establishment of a digital model that stores the local spatial transformation parameterization correspondences between the topographic datasets is realized. Along with designated interpolation concepts, this complete process ensures that the visualized transition from one topographic dataset to the other via the quantified correspondences is smooth and continuous, while maintaining morphological and topological relations.
Highlights
Time-dependent visualization of changes in physical entities, such as topographic datasets that present morphologic alterations, requires mutually modelling a series of discrete given datasets
The reality of physical phenomenon occurring during acquisition times together with existing geometric and morphologic ambiguities add to modelling the complexity of the observed datasets
This paper introduces a novel hierarchical modelling algorithm that establishes a quantitative mutual modelling via sets of spatial correspondences that best describes the morphologic changes occurred; it enables us to carry out more precise, true-to-nature and realistic animation visualization and simulation tasks
Summary
Time-dependent visualization of changes in physical entities, such as topographic datasets that present morphologic alterations, requires mutually modelling a series of discrete given datasets. This paper introduces a novel hierarchical modelling algorithm that establishes a quantitative mutual modelling via sets of spatial correspondences that best describes the morphologic changes occurred; it enables us to carry out more precise, true-to-nature and realistic animation visualization and simulation tasks. These are essential for a reliable 3D Geographic Information. This has to be achieved with minimal user intervention, while maintaining the properties of objects to maximum extent possible [5] It is obvious, that achieving this challenge and the abovementioned criteria is intensified when dealing with the complexities of topographic representations required for real (virtual-reality) GIS and mapping applications, such as morphologic alterations simulations
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