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An Overview of Image Segmentation Based on Pulse-Coupled Neural Network
Abstract Recent many researchers focus on image segmentation methods due to the rapid development of artificial intelligence technology. Hereinto, pulse-coupled neural network (PCNN) has a great potential based on the properties of neuronal activities. This paper elaborates internal behaviors of the PCNN to exhibit its image segmentation abilities. There are three significant parts: dynamic properties, parameter setting and complex PCNN. Further, we systematically provide the related segmentation contents of the PCNN, and hope to help researchers to understand suitable segmentation applications of PCNN models. Many corresponding examples are also used to exhibit PCNN segmentation effects.
An Overview of Image Segmentation Based on Pulse-Coupled Neural Network
Abstract Recent many researchers focus on image segmentation methods due to the rapid development of artificial intelligence technology. Hereinto, pulse-coupled neural network (PCNN) has a great potential based on the properties of neuronal activities. This paper elaborates internal behaviors of the PCNN to exhibit its image segmentation abilities. There are three significant parts: dynamic properties, parameter setting and complex PCNN. Further, we systematically provide the related segmentation contents of the PCNN, and hope to help researchers to understand suitable segmentation applications of PCNN models. Many corresponding examples are also used to exhibit PCNN segmentation effects.
An Overview of Image Segmentation Based on Pulse-Coupled Neural Network
Lian, Jing (author) / Yang, Zhen (author) / Liu, Jizhao (author) / Sun, Wenhao (author) / Zheng, Li (author) / Du, Xiaogang (author) / Yi, Zetong (author) / Shi, Bin (author) / Ma, Yide (author)
2019
Article (Journal)
Electronic Resource
English
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