| 本书是介绍脉冲耦合神经网络在图像滤波、图像分割、图像编码、图像增强、图像融合、特征提取和优化组合等方面的应用。书中包含了具体的图像处理算法、应用实例以及源代码，帮助读者建立脉冲耦合神经网络在图像处理中的应用。该书可供各大专院校作为教材使用，也可供从事相关工作的人员作为参考用书使用。 |
| Prof. Yide Ma conducts research on intelligent information processing, biomedical image processing, and embedded system development at the School of Information Science and Engineering, Lanzhou University, China. |
| 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 Filter 2.1.2 Median Filter 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 6.1 PCNN and Image Fusion 6.1.I Preliminary of Image Fusion 6.1.2 Applications in Image Fusion 6.2 Medical Image Fusion 6.2.1 Description of Model 6.2.2 Image Fusion Algorithm 6.2.3 Experimental Results and Analysis 6.3 Multi-focus Image Fusion 6.3.1 Dual-channel PCNN 6.3.2 Image Sharpness Measure 6.3.3 Principle of Fusion Algorithm 6.3.4 Implementation of Multi-focus Image Fusion 6.3.5 Experimental Results and Analysis Summary References Chapter 7 Feature Extraction 7.1 Feature Extraction with PCNN 7.1.1 Time Series 7.1.2 Entropy Series 7.1.3 Statistic Series 7.1.4 Orthogonal Transform 7.2 Noise Image Recognition 7.2.1 Feature Extraction Using PCNN 7.2.2 Experimental Results and Analysis 7.3 Image Recognition Using Barycenter of Histogram Vector 7.4 Invariant Texture Retrieval 7.4.1 Texture Feature Extraction Using PCNN 7.4.2 Experimental Results and Analysis 7.5 Iris Recognition System 7.5.1 Iris Recognition 7.5.2 Iris Feature Extraction Using PCNN 7.5.3 Experimental Results and Analysis Summary References Chapter 8 Combinatorial Optimization 8.1 Modified PCNN Based on Auto-wave 8.1.1 Auto-wave Nature of PCNN 8.1.2 Auto-wave Neural Network 8.1.3 Tristate Cascading Pulse Couple Neural Network 8.2 The Shortest Path Problem 8.2.1 Algorithm for Shortest Path Problems Based on TCPCNN 8.2.2 Experimental Results and Analysis 8.3 Traveling Salesman Problem 8.3.1 Algorithm for Optimal Problems Based on AWNN 8.3.2 Experimental Results and Analysis Summary References Chapter 9 FPGA Implementation of PCNN Algorithm 9.1 Fndamental Principle of PCNN Hardware Implementation 9.2 Altera DE2-70 Implementation of PCNN 9.2.1 PCNN Implementation Using Altera DE2-70 9.2.2 Experimental Results and Analysis Summary References Index |
内容加载中，请稍后...
商品评论（0条）