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Jingdong book

脉冲耦合神经网络及应用（国内英文版）

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Jingdong book

脉冲耦合神经网络及应用（国内英文版）

Name: 脉冲耦合神经网络及应用（国内英文版）
Brand: Jingdong book
SKU: 3118067341
Price: 31.25 USD
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《脉冲耦合神经网络及应用（国内英文版）》内容简介：Applications of Pulse-Coupled Neural Networks explores the fields of image processing, including image filtering, image segmentation, image fusion, image coding, image retrieval, and biometric recognition, and the role of pulse-coupled neural networks in these fields.
This book is intended for researchers and graduate students in artificial intelligence, pattern recognition, electronic engineering, and computer science.

Catalogue

Chapter 1 Pulse-Coupled Neural Networks
1.1Linking Field Model
1.2PCNN
1.3Modified PCNN
1.3.1Intersection Cortical Model
1.3.2Spiking Cortical Model
1.3.3Multi-channel PCNN
Summary
References
Chapter 2Image Filtering
2.1Traditional Filters
2.1.1Mean Filte
2.1.2Median Filte
2.1.3Morphological Filter
2.1.4Wiener Filter
2.2Impulse Noise Filtering
2.2.1Description of Algorithm Ⅰ
2.2.2Description of Algorithm Ⅱ
2.2.3Experimental Results and Analysis
2.3Gaussian Noise Filtering
2.3.1PCNNNI and Time Matrix
2.3.2Description of Algorithm Ⅲ
2.3.3Experimental Results and Analysis
Summary
References
Chapter 3Image Segmentation
3.1Traditional Methods and Evaluation Criteria
3.1.1Image Segmentation Using Arithmetic Mean
3.1.2Image Segmentation Using Entropy and Histogram
3.1.3Image Segmentation Using Maximum Between-cluster Variance
3.1.4Objective Evaluation Criteria
3.2Image Segmentation Using PCNN and Entropy
3.3Image Segmentation Using Simplified PCNN and GA
3.3.1Simplified PCNN Model
3.3.2Design of Application Scheme of GA
3.3.3Flow of Algorithm
3.3.4Experimental Results and Analysis
Summary
References
Chapter 4Image Coding
4.1Irregular Segmented Region Coding
4.1.1Coding of Contours Using Chain Code
4.1.2Basic Theories on Orthogonality
4.1.3Orthonormalizing Process of Basis Functions
4.1.4ISRC Coding and Decoding Framework
4.2Irregular Segmented Region Coding Based on PCNN
4.2.1Segmentation Method
4.2.2Experimental Results and Analysis
Summary
References
Chapter 5Image Enhancement
5.1Image Enhancement
5.1.1Image Enhancement in Spatial Domain
5.1.2Image Enhancement in Frequency Domain
5.1.3Histogram Equalization
5.2PCNN Time Matrix
5.2.1Human Visual Characteristics
5.2.2PCNN and Human Visual Characteristics
5.2.3PCNN Time Matrix
5.3Modified PCNN Model
5.4Image Enhancement Using PCNN Time Matrix
5.5Color Image Enhancement Using PCNN
Summary
References
Chapter 6Image Fusion
Chapter 7Feature Extraction
Chapter 8Combinatorial Optimization
Chapter 9FPGA Implementation of PCNN Algorithm
Index

Introduction

There is no more complicated, advantaged and powerful device than themammalian primate cortical visual system for image processing in nature.The pulse-coupled neural network （PCNN） is inspired from the investigationof pulse synchronization within the mammalian visual cortex, and has beenwidely applied to image processing and pattern recognition.
Visual cortex is the passage for brain to acquire information from eyesand a part of brain central nervous system. Several biological models basedon visual cortex were proposed through investigation of cat cortex and hadbeen applied to image processing.
The PCNN emulates the mammalian visual cortex, which is supposed tobe one of the most efficient image processing methods. The output of thePCNN is a series of pulse images which represent the fundamental featuresof original stimulus, such as edge, texture, and segment. Neurons receiveinputs from other neurons through synapses and are fired synchronously incertain regions, that is why the PCNN can be applied to image segmentation,smoothing, and coding. Another important feature of the PCNN is that thepulse images are able to be characterized to a unique invariant signature forthe image retrieval.
This book analyzes the PCNN in detail and presents some special appli-cations and corresponding results based on our own researches.
Contributions of the book have come from Hongjuan Zhang, RongchangZhao, Maojun Su, Dongmei Lin, Xiaojun Li, Guanzhu Xu, Xin Wang, Za-ifeng Zhang, Xiaowen Feng, Haibo Deng, Li Liu, Xiaozhe Xu, ChunliangQi, Chenghu Wu, Fei Shi, Zhibai Qian, Qing Liu, Min Yuan, Jiuwen Zhang,Yingjie Liu, Xiaolei Chen, and our graduate students at Circuit and SystemResearch Institute of Lanzhou University.

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Jingdong book

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