A framework for polysensometric multidimensional spatial visualization

Typically any single sensor instrument suffers from physical/observation constraints. This paper discusses a generalized framework, called polymorphic visual information fusion framework (PVIF) that can enable information from multiple sensors to be fused and compared to gain broader understanding of a target of observation in multidimensional space. An automated software system supporting comparative cognition has been developed to form 3D models based on the datasets from different sensors, such as XPS and LSCM. This fusion framework not only provides an information engineering based tool to overcome the limitations of individual sensor´s scope of observation but also provides a means where theoretical understanding surrounding a complex target can be mutually validated by comparative cognition about the object of interest and 3D model refinement. Some polysensometric data classification metrics are provided to measure the quality of input datasets for fusion visualization.