{'fraction_non_alphanumeric': 0.012387387387387387, 'fraction_numerical': 0.0, 'mean_word_length': 5.26056338028169, '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}}
A neuro-dynamic architecture for one shot learning of objects that uses both bottom-up recognition and top-down prediction

Learning to recognize objects from a small number of example views is a difficult problem of robot vision, of particular importance to assistance robots who are taught by human users. Here we present an approach that combines bottom-up recognition of matching patterns and top-down estimation of pose parameters in a recurrent loop that improves on previous efforts to reconcile invariance of recognition under view changes with discrimination among different objects. We demonstrate and evaluate the approach both in a service robotics implementation as well as on the COIL database. The robotic implementation highlights features of our approach that enable real-time pose tracking as well as recognition from views where figure ground segmentation is difficult.