A model-based self-adaptive approach to image processing

Abstract

Implementing image-processing systems can require significant effort and resources due to information volume and algorithm complexity. Model integrated computing (MIC) based image processing systems show promise in supporting solutions of these complex problems. While MIC has contributed to the advancement of performing complex image processing tasks on parallel-embedded systems, it has not addressed a challenging class of algorithms that adapt the image-processing algorithm based on the information or state of the image processing system. This proposed effort addresses creating an adaptive image-processing environment based on MIC that allows solutions of complex image processing problems to be built and executed rapidly. This effort involves creating a new modeling representation for image processing adaptation mechanisms. The proposed MIC-based adaptive image-processing environment generates a solution given the modeling constraints and executes it on a number of hardware architectures.