Saliency measures in cluttered IR images for ATR

Markus Mueller

doi:10.1117/12.352962

14 July 1999 Saliency measures in cluttered IR images for ATR

Markus Mueller

Proceedings Volume 3699, Targets and Backgrounds: Characterization and Representation V; (1999) https://doi.org/10.1117/12.352962
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

The paper presents approaches for the characterization of saliency with respect to MMO (Man-Made Object) detection at the example of vehicle detection in infrared (IR) images. The methodology is based on an extended evaluation of gradient direction histograms presented in earlier AeroSense (1996, 1997) symposia. 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 can be 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]. (beta) errors lead to an increased analysis workload in subsequent processing phases. In unfavorable cases much too many image domains are labeled and hence the ROI detection is ineffective. (alpha) errors are even more problematic since it is hard to compensate omissions in subsequent evaluation phases. 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 (alpha) and (beta) demands for application specific solutions.

Citation Download Citation

Markus Mueller "Saliency measures in cluttered IR images for ATR", Proc. SPIE 3699, Targets and Backgrounds: Characterization and Representation V, (14 July 1999); https://doi.org/10.1117/12.352962

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