{'fraction_non_alphanumeric': 0.013934970139349702, 'fraction_numerical': 0.0, 'mean_word_length': 5.362869198312236, '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}}
Exploiting dynamic aspects of visual perception for object recognition

When computer vision algorithms are applied to autonomous robots, several dynamic aspects of perception must be taken into account. Among the most important ones is the capability to modify the image acquisition parameters, in order to perform ocular saccades, or to visually track moving objects. This ability is provided by the alerting and attention mechanisms, which allow one to rapidly detect and locate potential visual targets. Another important dynamic aspect of perception is the difference in latencies of the neural signals arriving to cells in the visual cortex. Neurophysiological findings suggest for instance that stronger stimuli elicit earlier responses than weaker ones. In data processing terms, these signals represent a data flow of stimuli. The article describes a computer vision system that exploits these two types of dynamic mechanisms. First, two algorithms are proposed for rapidly detecting interesting parts of the input image; one of them acts on an image sequence, and extracts the regions containing moving objects. The other one acts on a static image, and selects the regions containing the most salient information. The second algorithm performs object recognition by exploiting the different latencies in the data flow of image primitives. The results shown suggest that the proposed mechanisms can be usefully integrated in robotic systems, so as to provide efficient perception action behaviors.