{'fraction_non_alphanumeric': 0.014840989399293287, 'fraction_numerical': 0.0014134275618374558, 'mean_word_length': 4.9, '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}}
Novel Concepts for Recognition and Representation of Structure in Spatio-Temporal Classes of Images

This paper discusses open problems and future research regarding the recognition and representation of structures in sequences of either 2D images or 3D data. All presented concepts aim at improving the recognition of structure in data (especially by decreasing the influence of noise) and at extending the representational power of known descriptors (within the scope of this paper graphs and skeletons). For the recognition of structure critical points of a shape may be computed. We present an approach to derive such critical points based on a combination of skeletons and local features along a skeleton. We further consider classes of data (for example a temporal sequence of images of an object), instead of a single data sample only. This so called co-analysis reduces the sensitivity of analysis to noise in the data. Moreover, a representative for a whole class can be provided. Temporal sequences may not only be used as a class of data in a coanalysis process focusing on the temporal aspect and changes of the data over time an analysis of these changes is needed. For this purpose we explore the possibility to analyse a shape over time and to derive a spatio-temporal representation. To extend the representational power of skeletons we further present an extension to skeletons using model fitting.