水准点(测量)
卷积神经网络
计算机科学
探测器
非线性系统
人工智能
边缘检测
模式识别(心理学)
粒子群优化
GSM演进的增强数据速率
尖峰神经网络
人工神经网络
算法
图像处理
图像(数学)
物理
电信
量子力学
大地测量学
地理
作者
Ronghao Xian,Rikong Lugu,Hong Peng,Qian Yang,Xiaoqiang Luo,Jun Wang
标识
DOI:10.1142/s0129065722500605
摘要
Nonlinear spiking neural P (NSNP) systems are a class of neural-like computational models inspired from the nonlinear mechanism of spiking neurons. NSNP systems have a distinguishing feature: nonlinear spiking mechanism. To handle edge detection of images, this paper proposes a variant, nonlinear spiking neural P (NSNP) systems with two outputs (TO), termed as NSNP-TO systems. Based on NSNP-TO system, an edge detection framework is developed, termed as ED-NSNP detector. The detection ability of ED-NSNP detector relies on two convolutional kernels. To obtain good detection performance, particle swarm optimization (PSO) is used to optimize the parameters of the two convolutional kernels. The proposed ED-NSNP detector is evaluated on several open benchmark images and compared with seven baseline edge detection methods. The comparison results indicate the availability and effectiveness of the proposed ED-NSNP detector.
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