Image complexity measurement for predicting target detectability

• Main Author: Peters, Richard Alan, 1956-
• Other Authors: Strickland, Robin N.
• Language: en_US
• Published: The University of Arizona. 1988
• Subjects: Image processing.; Computer vision.; Target acquisition.
• Online Access: http://hdl.handle.net/10150/282092

Designers of automatic target recognition algorithms (ATRs) need to compare the performance of different ATRs on a wide variety of imagery. The task would be greatly facilitated by an image complexity metric that correlates with the performance of a large number of ATRs. The ideal metric is independent of any specific ATR and does not require advance knowledge of true targets in the image. No currently used metric meets both these criteria. Complete independence of ATRs and prior target information is neither possible nor desirable since the metric must correlate with ATR performance. An image complexity metric that derives from the common characteristics of a large set of ATRs and the attributes of typical targets may be sufficiently general for ATR comparison. Many real-time, tactical ATRs operate on forward looking infrared (FLIR) imagery and identify, as potential targets, image regions of a specific size that are highly discernible by virtue of their contrast and edge strength. For such ATRs, an image complexity metric could be based on measurements of the mutual discernibility of image regions on various scales. This paper: (1) reviews ATR algorithms in the public domain literature and investigates the common characteristics of both the algorithms and the imagery on which they operate; (2) shows that complexity measurement requires a complete segmentation of the image based on these commonalities; (3) presents a new method of scale-specific image segmentation that uses the mask-driven close-open transform, a novel implementation of a morphological operator; (4) reviews edge detection for discernibility measurement; (5) surveys image complexity metrics in the current literature and discusses their limitations; (6) proposes a new local feature discernibility metric based on relative contrast and edge strength; (7) derives a new global image complexity metric based on the probability distribution of local metrics; (8) compares the metric to the output of a specific ATR; and (9) makes suggestions for further work.