计算机科学 ›› 2013, Vol. 40 ›› Issue (6): 291-294.

• 图形图像与模式识别 • 上一篇    下一篇

基于视觉信息的PCNN参数自适应设定及模型改进

赵彦明   

  1. 河北民族师范学院 承德067000
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受河北省教育科学“十二五”规划项目(11100053),河北省高等学校科学研究项目(Z2012127)资助

Adaptive Parameters Settings Method of PCNN Based on Visual Information and its Modified Model

ZHAO Yan-ming   

  • Online:2018-11-16 Published:2018-11-16

摘要: 脉冲耦合神经网络(PCNN)参数决定该模型在数字图像处理领域的应用。现阶段网络参数自适应设定是依据图像统计信息或网络自身结构。基于此,提出基于生物视觉信息的PCNN参数自适应设置方法及模型改进。该方法通过对生物视觉感知理论与PCNN网络性质的分析,揭示了视觉感知理论与PCNN网络参数M、W和β的同源性,给出依据视觉感知模型自适应设定PCNN网络参数W、M和β的方法,并设计出具有生物视觉特征的PCNN改进模型。实验验证了该模型的几何不变性,在基于内容的图像检索领域取得了良好效果。

关键词: 脉冲耦合神经网络,参数自适应设定,视觉感知理论,几何不变性

Abstract: The parameters of pulse dual neural network (PCNN) determine the application of the model in the field of digital image processing.But adaptive settings of network parameters are based on the information of image statistics or network structure.Based on this,the adaptive parameters settings method of PCNN based on visual information was proposed and model was improved.By analyzing the nature of the biological visual perception theory and PCNN network,the method reveals the homology of the theory of visual perception and PCNN network parameters M,W and β.The M,W and β of adaptive parameter setting method were given on the basis of visual perception model.The PCNN improvement model of Biological visual features was designed.The experiments verify the geometric invariance of the model.And it is proved that the model achieves good results at the field of Content-based image retrieval.

Key words: Pulse dual neural network,Parameters adaptive settings,Visual perception theory,Geometric invariance

[1] Eckhorn R,Reitboeck H J,Arndt M,et al.Feature Linking Via Synchronization Among Distributed Assem-blies:Simulation of Results from Cat Cortex[J].Neural Computation,1990,2(3):293-307
[2] Johnson J L,Padgett M L.PCNN Models and Application[J].IEEE Trans on Neural Net Works,1999,0(3):554-563
[3] 严春满,郭宝龙,马义德,等.一种新的基于双层PCNN的自适应图像分割算法[J].光电子.激光,2011,2(7):1103-1106
[4] 祝双武,郝重阳.一种基于改进型PCNN的织物疵点图像自适应分割方法[J].电子学报,2012,0(3):612-616
[5] 李海芳,尹清.视觉感知中特征捆绑建模方法的研究[J].计算机工程,2011,7(22):152-155
[6] 赵峙江,赵春晖,张志宏.一种新的PCNN模型参数估算方法[J].电子学报,2007,5(5):996-1000
[7] Kuntimad G,Ranganath H S.Perfect Image Segmentation Using Pulse Coupled Neural Networks[J].IEEE Trans.on Neural Networks,1999,10(3):591-598
[8] Li Min,Cai Wei,Tan Zheng.Adaptive Parameters Determination Method of Pulse Coupled Neural Network Based on Water Valley Area[A]∥Neural Information Processing pt.2[C].Hong Kong,2006:713-720
[9] 马义德,戴若兰,李廉.一种基于脉冲耦合神经网络和图像熵的自动图像分割方法[J].通信学报,2002,3(1):46-51
[10] Chen Yu-li,Park S-K,Ma Yi-de.A New Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation[J].IEEE Transactions on Neural Networks,2011,2(6):880-892
[11] 杨娜,陈后金,李艳凤.基于感受野-脉冲耦合神经网络模型的车辆图像分割算法[J].吉林大学学报,2012,0(7):506-560
[12] 邓翔宇,马义德.PCNN参数自适应设定及其模型的改进[J].Acta Electronica Senica,2012,0(5):956-964
[13] Johnson J L,Ranganath H,Kuntimad G.Pulse Coupled Neural Networks[M].Neural Networks and Pattern Recognition,San Diego,1998:1-56
[14] Bear M F,Connors B W,Paradiso M A.Neuroscience:Exploring the Brain(Second Edition)[M].Lippincott Williams & Wilkins,2001:473-482
[15] Plebe A.A model of angle selectivity development in visual area V2[J].Neurocomputing,2007,0:2060-2063
[16] 江友谊,余瑞星,宋军艳.基于ICM的局部不变特征提取方法[J].红外技术,2012(3):177-180
[17] 刘勍,许录平,马义德,等.基于脉冲耦合神经网络的图像NMI特征提取及检索方法[J].自动化学报,2010(7):931-938

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