{'fraction_non_alphanumeric': 0.02360248447204969, 'fraction_numerical': 0.0055900621118012426, 'mean_word_length': 4.966666666666667, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Clutter performance and confuser rejection on infrared data using distortion-invariant filters for ATR

We consider automatic target recognition (ATR) in infrared (IR) imagery using the minimum noise and correlation energy (MINACE) distortion-invariant filter (DIF). As in our prior work (SPIE 6566-03), we consider classification of true-class CAD targets and rejection of real clutter and unseen confuser CAD objects with range and full 360° aspect view variations. In this work, we address rejection of new UCIR bush clutter data. We also present performance scores for several different training and test cases with attention to filter capacity, i.e., the number of training images that can be included in one filter before performance on the test set deteriorates appreciably. We find that range rather than aspect view distortions seem to affect filter capacity more. Initial target contrast ratio tests are also presented. To more properly address clutter, in all tests we now form the magnitude of the output correlation plane before analysis. We also address when and why linear versus circular correlations are best. We also address DIF filter-synthesis and fast implementation for wide area "search" test regions. This introduces new issues concerning the region over which correlation plane energy is minimized in filter synthesis and the size of the FFT to use in tests. A key issue is that both training and tests should use the same procedures. This is vital for training and test metrics to be comparable. We distinguish between whether linear or circular correlation plane energy is minimized.