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lixj1982金虫 (小有名气)
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重金请虫友帮忙查询sci检索号 已有4人参与
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请大家帮忙查询一下以下三篇文章sci检索号，急用，最好有详单。不胜感激。 1. Multi-focus image fusion using PCNN. Pattern Recognition, 2010, 43:2003-2016. 2. Review of pulse-coupled neural networks. Image and Vision Computing, 2010, 28(1): 5-13. 4. Tri-state cascading pulse coupled neural network and its application in finding shortest path。 Neuron Network World，2009,19(6): 711-723. [ Last edited by lixj1982 on 2010-4-14 at 20:34 ] |
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glassman782
铁杆木虫 (知名作家)
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lixj1982(金币+2): 2010-04-14 22:09
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Multi-focus image fusion using PCNN 更多选项 作者: Wang ZB (Wang, Zhaobin)1,2, Ma YD (Ma, Yide)1, Gu J (Gu, Jason)2 来源出版物: PATTERN RECOGNITION 卷: 43 期: 6 页: 2003-2016 出版年: JUN 2010 被引频次: 0 参考文献: 26 引证关系图 摘要: This paper proposes a new method for multi-focus image fusion based on dual-channel pulse coupled neural networks (dual-channel PCNN). Compared with previous methods, our method does not decompose the input source images and need not employ more PCNNs or other algorithms such as DWT. This method employs the dual-channel PCNN to implement multi-focus image fusion. Two parallel source images are directly input into PCNN. Meanwhile focus measure is carried out for source images. According to results of focus measure, weighted coefficients are automatically adjusted. The rule of auto-adjusting depends on the specific transformation. Input images are combined in the dual-channel PCNN. Four group experiments are designed to testify the performance of the proposed method. Several existing methods are compared with our method. Experimental results show our presented method outperforms existing methods, in both visual effect and objective evaluation criteria. Finally, some practical applications are given further. (C) 2010 Elsevier Ltd. All rights reserved. 文献类型: Article 语言: English 作者关键词: PCNN; Image fusion; Focus measure KeyWords Plus: COUPLED NEURAL-NETWORK 通讯作者地址: Ma, YD (通讯作者), Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China 地址: 1. Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China 2. Dalhousie Univ, Dept Elect & Comp Engn, Halifax, NS B3J 2X4 Canada 电子邮件地址: zhaobin_wang@hotmail.com, ydma@lzu.edu.cn 基金资助致谢: 基金资助机构 授权号 National Natural Science Foundation of China 60872109 Program for New Century Excellent Talents in University NCET-06-0900 China Scholarship Fundamental Research Funds for the Central Universities of Lanzhou University in China Izujbky-2009-129 [显示基金资助信息] We thank the associate editor and the reviewers for their helpful and constructive suggestions. The authors also thank Ying Zhu for her support and help. This paper is jointly supported by National Natural Science Foundation of China (No.60872109), Program for New Century Excellent Talents in University (NCET-06-0900), China Scholarship, and the Fundamental Research Funds for the Central Universities of Lanzhou University in China ( Izujbky-2009-129). 出版商: ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 学科类别: Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic IDS 号: 574KJ ISSN: 0031-3203 DOI: 10.1016/j.patcog.2010.01.011 |
2楼2010-04-14 20:50:49
antiq
木虫 (知名作家)
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lixj1982(金币+1): 2010-04-14 22:15
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2. FN ISI Export Format VR 1.0 PT J AN 11176020 DT Journal Paper TI Review of pulse-coupled neural networks AU Zhaobin Wang Yide Ma Feiyan Cheng Lizhen Yang SO Image and Vision Computing PY 2010 PD January 2010 VL 28 IS 1 JI Image Vis. Comput. (Netherlands) BP 5 EP 13 PS 5-13 DI 10.1016/j.imavis.2009.06.007 LA English AB This paper reviews the research status of pulse-coupled neural networks (PCNN) in the past decade. Considering there are too many publications about the PCNN, we summarize main approaches and point out interesting parts of the PCNN researches rather than contemplate to go into details of particular algorithms or describe results of comparative experiments. First, the current status of the PCNN and some modified models are briefly introduced. Second, we review the PCNN applications in the field of image processing (e.g. image segmentation, image enhancement, image fusion, object and edge detection, pattern recognition, etc.), then applications in other fields also are mentioned. Subsequently, some existing problems are summarized, while we give some suggestions for the solutions to some puzzles. Finally, the trend of the PCNN is pointed out. [All rights reserved Elsevier]. DE Bibliography, Practical/ image processing; neural nets/ pulse-coupled neural networks; image processing/ B6135 Optical, image and video signal processing C5260B Computer vision and image processing techniques C5290 Neural computing techniques C1 Zhaobin Wang; Yide Ma; Feiyan Cheng; Lizhen Yang; Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou, China PU Elsevier Science B.V. PV Netherlands NR 133 CO IVCODK SN 0262-8856 ID [S0262-8856(09)00134-6],[10.1016/j.imavis.2009.06.007] UT INSPEC:11176020 ER |
3楼2010-04-14 20:51:28
glassman782
铁杆木虫 (知名作家)
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lixj1982(金币+2): 2010-04-14 22:09
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Review of pulse-coupled neural networks 更多选项 作者: Wang ZB (Wang, Zhaobin)1, Ma YD (Ma, Yide)1, Cheng FY (Cheng, Feiyan)1, Yang LZ (Yang, Lizhen)1 来源出版物: IMAGE AND VISION COMPUTING 卷: 28 期: 1 页: 5-13 出版年: JAN 2010 被引频次: 0 参考文献: 133 引证关系图 摘要: This paper reviews the research status of pulse-coupled neural networks (PCNN) in the past decade. Considering there are too many publications about the PCNN, we summarize main approaches and point out interesting parts of the PCNN researches rather than contemplate to go into details of particular algorithms or describe results of comparative experiments. First, the current status of the PCNN and some modified models are briefly introduced. Second, we review the PCNN applications in the field of image processing (e.g. image segmentation, image enhancement, image fusion, object and edge detection, pattern recognition, etc.), then applications in other fields also are mentioned. Subsequently, some existing problems are summarized, while we give some suggestions for the solutions to some puzzles. Finally, the trend of the PCNN is pointed out. (C) 2009 Elsevier B.V. All rights reserved. 文献类型: Review 语言: English 作者关键词: Pulse-coupled neural networks (PCNN); Image processing; Artificial neural network KeyWords Plus: INTERSECTING CORTICAL MODEL; IMAGE FUSION; PATTERN-RECOGNITION; SEGMENTATION; PCNN; CLASSIFICATION; ROTATION; REMOVAL; NOISE; SCALE 通讯作者地址: Ma, YD (通讯作者), Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu Peoples R China 地址: 1. Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu Peoples R China 电子邮件地址: zhaobin_wang@hotmail.com, ydma@lzu.edu.cn 基金资助致谢: 基金资助机构 授权号 National Natural Science Foundation of China 60572011 60872109 Program for New Century Excellent Talents in University NCET-06-0900 China Scholarship [显示基金资助信息] We thank the associate editor, reviewers, and people, who help us to improve the paper, for their helpful and constructive suggestions. The authors also thank Ying Zhu for her support and help. This paper is jointly supported by National Natural Science Foundation of China (Nos. 60572011 and 60872109), Program for New Century Excellent Talents in University (NCET-06-0900), and China Scholarship. 出版商: ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 学科类别: Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Optics IDS 号: 534KK ISSN: 0262-8856 DOI: 10.1016/j.imavis.2009.06.007 |
4楼2010-04-14 20:51:56
5楼2010-04-14 20:52:27
glassman782
铁杆木虫 (知名作家)
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lixj1982(金币+2): 2010-04-14 22:09
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TRI-STATE CASCADING PULSE COUPLED NEURAL NETWORK AND ITS APPLICATION IN FINDING SHORTEST PATH 更多选项 作者: Zhao RC (Zhao Rongchang)1, Ma YD (Ma Yide)1, Zhan K (Zhan Kun)1 来源出版物: NEURAL NETWORK WORLD 卷: 19 期: 6 页: 711-723 出版年: 2009 被引频次: 0 参考文献: 14 引证关系图 摘要: To increase the computing speed of neural networks by means of parallel performance, a new mode of neural network, named Tri-state Cascading Pulse Coupled Neural Network (TCPCNN), is presented in this paper, which takes the ideas of three-state and pipelining used in circuit designing into neural network, and creates new neuron with three states: sub-firing, firing and inhibition. The proposed model can transmit signals in parallel way, as it is inspired not only in the direction of auto-wave propagation but also in its transverse direction in neural network. In this paper, TCPCNN is applied to find the shortest path, and the experimental results indicate that the algorithm has lower computational complexity, higher accuracy, and secured full-scale searching. Furthermore, it has little dependence on initial conditions and parameters. The algorithm is tested by some experiments, and its results are compared with some other classical algorithms - Dijkstra algorithm, Bellman-Ford algorithm and a new algorithm using pulse coupled neural networks. 文献类型: Article 语言: English 作者关键词: Optimization problem; neural network; pcnn; shortest path; auto-wave; parallel process; tri-state cascading pulse coupled neural network 通讯作者地址: Zhao, RC (通讯作者), Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu Peoples R China 地址: 1. Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu Peoples R China 电子邮件地址: Byrons.zhao@gmail.com, ydma@lzu.edu.cn 基金资助致谢: 基金资助机构 授权号 National Science Foundation of China 60572011 60872109 Program for New Century Excellent Talents in University NCET-06-0900 [显示基金资助信息] The authors thank the associate editor and the anonymous reviewers for their careful work and valuable suggestions. We are also very grateful to Jason Gu, Ph.D in Dalhousie University, who kindly helped us to correct mistakes in the paper. Moreover, the work is supported by the National Science Foundation of China under the Grant No. 60572011 and No. 60872109, and Program for New Century Excellent Talents in University under No. NCET-06-0900. 出版商: ACAD SCIENCES CZECH REPUBLIC, INST COMPUTER SCIENCE, POD VODARENSKOU VEZI 2, 182 07 PRAGUE 8, 00000, CZECH REPUBLIC 学科类别: Computer Science, Artificial Intelligence; Neurosciences IDS 号: 545KB ISSN: 1210-0552 |
6楼2010-04-14 20:53:01
antiq
木虫 (知名作家)
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7楼2010-04-14 20:56:35
xmuchong6561
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8楼2010-04-15 06:24:54
