Saliency measures in cluttered IR images for ATR

The detection of conspicuous image domains (ROI - Regions of Interest) is an early signal near operation in the process of automated detection and recognition of MMO used in ATR (Automatic Target Recognition) algorithm chains. For this purpose the ROI detection has to be fast and reliable. It is used as an efficient data reduction device to speed up subsequent exploitation phases without loss of relevant information. Usually two complementary error classes are distinguished: class alpha (an interesting image domain was not detected) and class beta (an irrelevant image domain (clutter) has been labeled), P errors lead to an increased analysis workload in subsequent exploitation phases. In unfavorable cases much too many image domains are labeled and hence the ROI detection is ineffective. a errors are even more problematic since omissions cannot be compensated in subsequent evaluation. The quality (efficiency and effectiveness) of the MMO detection restricts the ultimate achievable system performance and hence determines the possible application fields (e.g. on-board or ground based ATR). The optimization trade off between a and P demands for application specific solutions