Image Processing using Pulse-Coupled Neural Networks: Applications in Python [Hardcover]

Image processing algorithms based on the mammalian visual cortex are powerful tools for extraction information and manipulating images. This book reviews the neural theory and translates them into digital models. Applications are given in areas of image recognition, foveation, image fusion and information extraction.
The third edition reflects renewed international interest in pulse image processing with updated sections presenting several newly developed applications. This edition also introduces a suite of Python scripts that assist readers in replicating results presented in the text and to further develop their own applications.This book reviews the neural theory and translates them into digital models. It gives applications in image recognition, foveation, image fusion and information extraction, and includes Python scripts that help readers replicate results presented in the text.Biological Models.- Programming in Python.- NumPy, SciPy and Python Image Library.- The PCNN and ICM.- Image Analysis.- Feedback and Isolation.- Recognition and Classification.- Texture Recognition.- Color and Multiple Channels.- Image Signatures.- Logic.Thomas Lindblad got his Ph.D. at the University of Stockholm in 1974, became associate professor two years later. Dr. Lindblad started his research career in nuclear structure and particle physics with a special interest in detector systems. He soon became the head of the section for Measuring Techniques and Information Processing at the Manne Siegbahn Institute of Physics. He was a frequent visitor to laboratories in Oak Ridge, Berkeley, Orsay, Grenoble and Darmstadt. Later professor Lindblad took a special interest in teaching, started Environmental Physics at the Royal Institute of Technology, Department of Physics, where he also was the director of undergraduate studies for several years. During this time his research interests focused on sensor techniques of different kinds and feature extraction of torrent data flows. lă