{'fraction_non_alphanumeric': 0.020903010033444816, 'fraction_numerical': 0.002508361204013378, 'mean_word_length': 4.955223880597015, '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}}
Edge detection from edge knots in live plant image processing

This paper proposes an optimization model for extracting edges in gray-scale images. The model sufficiently utilizes the gray-level information in a pair of orthogonal directions at each considered pixel. The model has three major features in its novelty: (1) Emphasizing the globality of traditional local features; (2) Being a generalized case of the classical snake models; and (3) Offering a theoretical interpretation to the setting of the parameters for the method based on the Simulation of Particle Motion in a Vector image Field (SPMVIF). Our Edge Detection from Edge Knots (EDEK) model can be divided into two stages: the generation ofedge knots on or near edges and a propagation process for producing complete edges from these edge knots. One advantage ofour approach is that the propagation process does not depend on any control parameters. The EDEK model is suitable for live plant image processing, which is demonstrated by a number of simulation results on the edge detection of live plant images. Our model is simple in computing and robust, and can perform very well even in situations where high curvature exists.