A Novel Defect Segmentation for Pulse Infrared Images Based on Improved Fuzzy C-Means Algorithm With Weighted Distance

Lin, Jing; Han, Minghong; Guang-hu, LI; Wu, Yaman

doi:10.1109/tmag.2022.3231133

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Artificial intelligence is the study of the laws of human intelligence activities, the construction of artificial systems with a certain level of intelligence, and the study of how to enable computers to perform tasks that previously required human intelligence, that is, the study of how to apply the basic theories, methods, and techniques of computer software and hardware to simulate certain intelligent behaviors of humans. For example, fuzzy set theory can be applied to a wide range of fields with incomplete or imprecise image segmentation, such as the fuzzy c-means method [34][35][36]. Wavelet transform is a local transformation of space and frequency, which can effectively extract information from the original images [36,37].…”

Section: Related Workmentioning

confidence: 99%

Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

Cai,

Li,

Zhang

et al. 2023

Applied Sciences

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Traditional artificial intelligence algorithms are prone to falling into local optima when solving threshold segmentation problems. Here, a novel artificial Physarum polycephalum colony algorithm is proposed to help us solve the difficult problem. First, the algorithm methodology of an artificial Physarum polycephalum colony algorithm is described to search for the optimal solutions by expansion and contraction of a lot of artificial hyphae. Different artificial Physarum polycephalum can learn from each other and produce more hyphae in expansion. In contraction, the artificial Physarum polycephalum colony can select the best hyphae with high fitness through a quick sort algorithm, but the other hyphae with low fitness will be absorbed and disappear. Second, a fitness function is modeled based on Kapur’s entropy for the proposed artificial Physarum polycephalum colony algorithm to search for optimal threshold segmentation solutions. Third, a series of benchmark experiments are implemented to test the proposed artificial Physarum polycephalum colony algorithm, and some state-of-the-art approaches are employed for comparison. The experimental results verified that the proposed algorithm can obtain better accuracy and convergence speed, and is not easier to fall into the local optimal solution too early.

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