DATA MODELING IN SCIENTIFIC IMAGES USING SIMULATED ANNEALING

Abstract

One way to measure objects in some classes of scientific image is to model the objects by functions and then make the measurements on the functions. This works especially well for images that contain information in the form of relationships between grey level pixels. Here, the use of a Moffat function as a data model is explored, as is the use of simulated annealing to fit many instances of this function to the data in the image. Two examples are presented: stellar photometry, a natural application for the Moffat function, and reading DNA sequencing gels.