脉冲耦合神经网络及应用：Applications of Pulse Coupled Neural Networks（国内英文版）

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