{'fraction_non_alphanumeric': 0.022026431718061675, 'fraction_numerical': 0.005873715124816446, 'mean_word_length': 5.089285714285714, '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 proposal of biologically inspired hierarchical approach to object recognition

In this article a biologically-inspired algorithm for object recognition is presented. The approach is based on a hierarchical HMAX cortex model that was initially proposed by Riesenhuber and Poggio [12] and later extended by Serre et al [13]. The results show that despite the modification that were undertaken to simplify the HMAX model (in order to make it feasible for a real-time solutions) it is possible to achieve high effectiveness for a one-class detection problems. Moreover, it is also demonstrated how the proposed algorithm can be successfully deployed on a low-cost Android smartphone.