Heterogeneous SPCNN and its application in image segmentation

被引：24
作者：
Yang, Zhen [1 ]
Lian, Jing [1 ,2 ]
Li, Shouliang [1 ]
Guo, Yanan [1 ]
Qi, Yunliang [1 ]
Ma, Yide [1 ]
机构：
[1] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China
[2] Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Gansu, Peoples R China
来源：
NEUROCOMPUTING | 2018年 / 285卷
关键词：
Heterogeneous simplified pulse coupled neural network; Parameter setting; Image segmentation; Evaluation index; COUPLED NEURAL-NETWORK; LINKING; FUSION;
D O I：
10.1016/j.neucom.2018.01.044
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Based on the fact that actual cerebral cortex has different structure, a new heterogeneous simplified pulse coupled neural network (HSPCNN) model is proposed in this paper for image segmentation. HSPCNN is constructed with several simplified pulse coupled neural network (SPCNN) models, which have different parameters corresponding to different neurons. An image is segmented by HSPCNN into several regions according to their gray levels. Moreover, the parameter of HSPCNN is set automatically in this paper, the experimental segmentation results of the gray natural images from the Berkeley Segmentation Dataset (BSD 300) show the validity and efficiency of the proposed segmentation method. Finally, an evaluation index is proposed to measure the segmentation result. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：196 / 203
页数：8
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