{'fraction_non_alphanumeric': 0.010067114093959731, 'fraction_numerical': 0.0, 'mean_word_length': 5.28421052631579, '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}}
Detecting dim point targets in infrared image sequences using probabilistic neural networks

In spite of many advances of IR imaging technology that have been achieved, the detection of dim point targets from IR clutter backgrounds still remains a key problem in real-time IR systems. We present a new detection scheme based on a linear detector and an improved probabilistic neural network classifier for small SNR, moving point targets detection in strong IR noise and clutter backgrounds. Computer simulation was conducted and simulation results confirmed the validity of the detection scheme.