{'fraction_non_alphanumeric': 0.01885014137606032, 'fraction_numerical': 0.0, 'mean_word_length': 5.0685714285714285, '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}}
Fast image mosaicing using incremental bags of binary words

Image mosaicing has gained increasing attention in the last few years, specially for robotic mapping applications. Due to the richness of the sensor data provided, several science fields require the creation of large-area image mosaics for further analysis. In this paper, we propose a novel and generic image mosaicing approach that can produce seamless composites under different configurations in a reasonable amount of time. Our approach is based on a multi-threaded architecture which allows us to execute the different steps of the algorithm simultaneously. To find the topology of the environment, we use a visual index based on a Bag-of-Binary-Words scheme, which is built in an online manner, and thus avoids the classic training step. Our approach is validated under different environments and camera configurations, showing that it can be used on several scenarios producing coherent results in all of them. Furthermore, the implementation of the algorithm is made public to the community.