{'fraction_non_alphanumeric': 0.015209125475285171, 'fraction_numerical': 0.0, 'mean_word_length': 4.988636363636363, '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}}
Sparse feature for hand gesture recognition: A comparative study

Any object recognition approach has a feature extraction/selection stage. Features should be carefully selected because they are used to object representation. The purpose of this work is to find good sparse features which can be used further to detect fingers and recognize hand gestures. The proposed features are edges, lines and salient regions extracted with Kadir and Brady detector. These features can be combined to define a large set of hand postures.