Applications of Pulse-Coupled Neural Networks
Springer Science & Business Media, Sep 2, 2011 - Computers - 199 pages
"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. 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.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Chapter 2 Image Filtering
Chapter 3 Image Segmentation
Chapter 4 Image Coding
Chapter 5 Image Enhancement
Chapter 6 Image Fusion
Other editions - View all
algorithm auto-wave AWNN barycenter binary image Cameraman coefficients combinatorial optimization Computer contours corresponding cortical model cross-entropy denotes dynamic threshold entropy sequence Euclidean distance evaluation experimental results feature extraction firing FPGA function fused image Gaussian noise graph gray level gray value gray-level histogram equalization IEEE Transactions image processing image retrieval image segmentation implemented impulse noise initialized threshold T0 input image intensity internal activity invariant texture retrieval iris feature iris image iris recognition ISRCCBP iterative number Laplacian Lena m-PCNN manually initialized threshold matrix maximum entropy median filter modified PCNN module multi-focus image fusion neuron node noisy image original image output parameters path problem pattern recognition PCNN PCNN model performance pixels proposed PSNR pulse images pulse-coupled neural network pyramid rotation segmented image shortest path shortest path problem shown in Fig signals source images stimulus Table texture feature transform visual cortex Wiener filter Zhang