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脉冲耦合神经网络及应用 版权信息
- ISBN：9787040279788
- 条形码：9787040279788 ; 978-7-04-027978-8
- 装帧：暂无
- 册数：暂无
- 重量：暂无
- 所属分类：>
脉冲耦合神经网络及应用 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.
脉冲耦合神经网络及应用 目录
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
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
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