{'fraction_non_alphanumeric': 0.0169061707523246, 'fraction_numerical': 0.0, 'mean_word_length': 5.103092783505154, 'pattern_counts': {'":': 0, '<': 2, '<?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}}
Knowledge-Based Silhouette Detection

A general-purpose neural model that challenges image understanding is presented in this paper. The model incorporates accumulative computation, lateral interaction and double time scale, and can be considered as biologically plausible. The model uses at global time scale t and in form of accumulative computation all the necessary mechanisms to detect movement from the grey level change at each pixel of the image. The information on the detected motion is useful as part of an object’s shape can be obtained. On a second time scale base T<<t, and by means of lateral interaction of each element with its neighbours, other parts of the moving object are also considered, even when no variation in grey level is detected on these parts. After introducing the general concepts of the model denominated Lateral Interaction in Accumulative Computation, the model is applied to the problem of silhouette detection of all moving elements in an indefinite sequence of images. The model is lastly compared to the most important current knowledge on motion analysis showing, this way its suitability to most well-known problems in silhouette detection.