An Image Metric-Based ATR Performance Prediction Testbed
Autor: | Scott K. Ralph, John M. Irvine, David Vanstone, Magnus Snorrason, Mark R. Stevens |
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Rok vydání: | 2006 |
Předmět: |
Contextual image classification
Computer science business.industry Testbed Cognitive neuroscience of visual object recognition Image segmentation Machine learning computer.software_genre Constant false alarm rate Metadata Performance prediction Systems design Artificial intelligence business computer |
Zdroj: | AIPR |
ISSN: | 1550-5219 |
DOI: | 10.1109/aipr.2006.13 |
Popis: | Automatic target detection (ATD) systems process imagery to detect and locate targets in imagery in support of a variety of military missions. Accurate prediction of ATD performance would assist in system design and trade stud-ies, collection management, and mission planning. A need exists for ATD performance prediction based exclusively on information available from the imagery and its associated metadata. We present a predictor based on image measures quantifying the intrinsic ATD difficulty on an image. The modeling effort consists of two phases: a learn-ing phase, where image measures are computed for a set of test images, the ATD performance is measured, and a prediction model is developed; and a second phase to test and validate performance prediction. The learning phase produces a mapping, valid across various ATR algorithms, which is even applicable when no image truth is avail-able (e.g., when evaluating denied area imagery). The testbed has plug-in capability to allow rapid evaluation of new ATR algorithms. The image measures employed in the model include: statistics derived from a constant false alarm rate (CFAR) processor, the Power Spectrum Signature, and others. We present a performance predictor using a trained classifier ATD that was constructed using GENIE, a tool developed at Los Alamos National Laboratory. The paper concludes with a discussion of future research. |
Databáze: | OpenAIRE |
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