特征连接模型及其应用

发布时间：2018-06-25 17:50

本文选题：特征连接模型 + 图像增强　；参考：《兰州大学》2017年硕士论文

【摘要】：脉冲耦合神经网络(Pulsed Coupled Neural Networks,PCNN)是第三代神经网络的典型代表,演化自哺乳动物视觉皮层系统的同步脉冲发放现象.在研究PCNN的基础上,我们提出了特征连接模型(Feature Linking Model,FLM),利用FLM的赋时矩阵和单通工作方式进行图像处理.首先,我们提出了FLM,该模型有反馈输入和连接输入两个输入端,它与PCNN有相似的结构,但是在PCNN中有三个漏电积分器而FLM只有两个漏电积分器,因此FLM较PCNN简单.我们发现,当阈值呈现指数衰减时,FLM的赋时矩阵和刺激输入之间呈现一个对数关系,并且通过单通工作方式记录了脉冲发生的时间.FLM中的全局抑制项,提高了同一个区域神经元的同步性和不同区域神经元之间的异步性.另外,γ带振荡启发的连接调节机制和动态阈值特性,使得FLM更接近于生物神经元特性.此外,受到生物神经学支持的赋时矩阵也是本文研究的重点.其次,我们提出了FLM的单通工作方式,该工作方式可以使得所有的神经元只能点火一次,且保证所有的神经元都能够点火.我们利用FLM的单通工作方式获得了赋时矩阵,这为本文提出的图像处理算法提供了基础.此外,FLM是通过其两种突触输入来获得同步脉冲的,我们分别介绍了这两种突触的调节机制和这两种突触的波形传播形式.基于FLM的赋时矩阵,结合同步特性,我们提出了FLM图像增强、图像分割和图像复原三种方法,并详细介绍了每种方法的预处理操作、算法的具体实现过程、参数的设定原理和与其它方法的比较实验等,最后我们从主观和客观两个方面对本文的算法进行评价,评价结果证明了本文提出的算法的优越性.
[Abstract]:Pulse coupled neural network (PCNN) is a typical representation of the third generation neural network, which evolves from the phenomenon of synchronous pulse firing in mammalian visual cortex system. Based on the study of PCNN, we propose a feature linking Model (FLM), which uses the time matrix of FLM and the single way to process the image. Firstly, we propose FLM, which has two input terminals: feedback input and connection input. It has a similar structure to PCNN, but there are three leakage integrators in PCNN and only two leakage integrators in FLM, so FLM is simpler than PCNN. We find that there is a logarithmic relationship between the time matrix of the FLM and the stimulus input when the threshold is exponentially attenuated, and the global suppression term in the pulse generation time. FLM is recorded by a single way. The synchronization of neurons in the same region and the asynchronism among neurons in different regions are improved. In addition, the connection regulation mechanism and the dynamic threshold characteristics of 纬 -band oscillation elicited the FLM to be closer to the biological neuron characteristics. In addition, the time matrix supported by biological neurology is also the focus of this paper. Secondly, we propose a single way of FLM, which can make all neurons light fire only once, and ensure that all neurons can light fire. We obtain the time matrix by using the single way of FLM, which provides the basis for the image processing algorithm proposed in this paper. In addition, the two synaptic inputs of FLM are used to obtain the synchronous pulse. We introduce the modulation mechanism of these two synapses and the waveform transmission forms of these two synapses, respectively. Based on the timed matrix of FLM and the synchronization characteristics, we propose three methods of FLM image enhancement, image segmentation and image restoration, and introduce the preprocessing operation of each method and the implementation process of the algorithm in detail. Finally, we evaluate the algorithm from the subjective and objective aspects, and the evaluation results prove the superiority of the proposed algorithm.
【学位授予单位】：兰州大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP183

【参考文献】