An Overview of Image Segmentation Based on Pulse-Coupled Neural Network

被引：36

作者：

Lian, Jing ^{[1
,2
]}

Yang, Zhen ^{[1
,2
]}

Liu, Jizhao ^{[3
]}

Sun, Wenhao ^{[1
,2
]}

Zheng, Li ^{[1
]}

Du, Xiaogang ^{[1
]}

Yi, Zetong ^{[1
]}

Shi, Bin ^{[4
]}

Ma, Yide ^{[2
]}

机构：

[1] Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, 88 West Anning Rd, Lanzhou 730070, Gansu, Peoples R China

[2] Lanzhou Univ, Sch Informat Sci & Engn, 222 South Tianshui Rd, Lanzhou 730000, Gansu, Peoples R China

[3] Sun Yat Sen Univ, Sch Data Sci & Comp Sci, 132 East Outer Ring Rd, Guangzhou 510275, Guangdong, Peoples R China

[4] Gansu Prov Hosp, Equipment Management Dept, 204 West Donggang Rd, Lanzhou 730000, Gansu, Peoples R China

来源：

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING | 2021年 / 28卷 / 02期

基金：

中国国家自然科学基金;

关键词：

COARSE-TO-FINE; SIMPLIFIED PCNN; AUTOMATIC SEGMENTATION; ADAPTIVE PCNN; MODEL; RECOGNITION; LINKING; ALGORITHM; EXTRACTION; DESIGN;

D O I：

10.1007/s11831-019-09381-5

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：387 / 403

页数：17

共 114 条

[1]

Ali JMH, 2006, NEURAL NETW WORLD, V16, P129

[2]

[Anonymous], 1994, DIGITAL MAMMO, DOI DOI 10.1007/S11999-016-4732-4

[3]

[Anonymous], 1989, CAN J MICROBIOL

[4] Automatic design of pulse coupled neurons for image segmentation [J].

Berg, Henrik ;

Olsson, Roland ;

Lindblad, Thomas ;

Chilo, Jose .

NEUROCOMPUTING, 2008, 71 (10-12) :1980-1993

[5]

Bi YW, 2004, LECT NOTES COMPUT SC, V3174, P405

[6] Bio-inspired architecture for static object segmentation in time varying background models from video sequences [J].

Chacon-Murguia, Mario I. ;

Ramirez-Quintana, Juan A. .

NEUROCOMPUTING, 2018, 275 :1846-1860

[7]

Chen N, 2007, PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, P2832

[8] Region-Based Object Recognition by Color Segmentation Using a Simplified PCNN [J].

Chen, Yuli ;

Ma, Yide ;

Kim, Dong Hwan ;

Park, Sung-Kee .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 26 (08) :1682-1697

[9] A New Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation [J].

Chen, Yuli ;

Park, Sung-Kee ;

Ma, Yide ;

Ala, Rajeshkanna .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2011, 22 (06) :880-892

[10] Research on crack detection applications of improved PCNN algorithm in moi nondestructive test method [J].

Cheng, Yuhua ;

Tian, Lulu ;

Yin, Chun ;

Huang, Xuegang ;

Cao, Jiuwen ;

Bai, Libing .

NEUROCOMPUTING, 2018, 277 :249-259

← 1 2 3 4 5 6 7 8 9 10 →