Slow Time Variant Image Visualization

When changes occur in complex images that involve minute details, it is usually a daunting task to track the changes that may have occurred between two time periods. We will be presenting a method for identifying these changes through digitizing; anchoring, reorienting and subtracting, successive images, and compiling an animated sequence of image difference that will include only the changes. This method will save time, and effort and will more importantly provide an accurate depiction of changes. This can also be applied to satellite images of cities, forests, and mountain ranges. Also, it can be applied to track down the material fatigue images that are hard to identify by the naked yet. Key-Words: image visualization, data mining, data manipulation, data streams, rule mining, spatial temporal data mining.