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ePub Image Processing Using Pulse-Coupled Neural Networks download

by Jason M. Kinser,Thomas Lindblad

ePub Image Processing Using Pulse-Coupled Neural Networks download
Author:
Jason M. Kinser,Thomas Lindblad
ISBN13:
978-3540242185
ISBN:
354024218X
Language:
Publisher:
Springer; 2nd edition (September 13, 2005)
Category:
Subcategory:
Computer Science
ePub file:
1264 kb
Fb2 file:
1541 kb
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Rating:
4.3
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798

Pulse-coupled networks or pulse-coupled neural networks (PCNNs) are neural models proposed by modeling a cat's visual cortex, and developed for high-performance biomimetic image processing.

Pulse-coupled networks or pulse-coupled neural networks (PCNNs) are neural models proposed by modeling a cat's visual cortex, and developed for high-performance biomimetic image processing. In 1989, Eckhorn introduced a neural model to emulate the mechanism of cat's visual cortex. The Eckhorn model provided a simple and effective tool for studying small mammal’s visual cortex, and was soon recognized as having significant application potential in image processing.

This is the first book to explain and demonstrate the tremendous ability of Pulse-Coupled Neural Networks (PCNNs) when applied to the field of image processing. PCNNs and their derivatives are biologically inspired models that are powerful tools for extracting texture.

Image Processing using Pulse-Coupled Neural Networks: Applications in Python Hardcover – 14 May 2013. by Thomas Lindblad (Author), Jason M. Kinser (Author). Applications are given in areas of image recognition, foveation, image fusion and information extraction. He soon became the head of the section for Measuring Techniques and Information Processing at the Manne Siegbahn Institute of Physics.

Автор: Thomas Lindblad; Jason M. Kinser Название: Image Processing using .

Thomas Lindblad Jason M. Kinser. Image processing by electronic means has been a very active field for decades. The Pulse-Coupled Neural Network The Pulse-Coupled Neural Network is to a very large extent based on the Eckhorn model except for a few minor modifications required by digitisation. The early experiments demonstrated that the PCNN could process images such output was invariant to images that were shifted, rotated, scaled, and skewed.

Pulse-coupled neural network (PCNN), which simulates the synchronous oscillation phenomenon in the visual cortex of small mammals, has become a useful model for image processing

Pulse-coupled neural network (PCNN), which simulates the synchronous oscillation phenomenon in the visual cortex of small mammals, has become a useful model for image processing. In the model, several parameters were usually required to properly set for adjusting the behavior of neurons. However, undesired behavior may occur owing to inappropriate parameters setting.

This model represents neural activity as coupled oscillators with two diffusion terms. Rybak Model Independently, Rybak studied the visual cortex of the guinea pig and found similar neural interactions.

Thomas Lindblad (author), Jason M.

Image Processing Using Pulse-Coupled Neural Networks: Applications in Python by. Thomas Lindblad, Jason M Kinser

Image Processing Using Pulse-Coupled Neural Networks: Applications in Python by. Thomas Lindblad, Jason M Kinser.

This is the first book to explain and demonstrate the tremendous ability of Pulse-Coupled Neural Networks (PCNNs) when applied to the field of image processing. PCNNs and their derivatives are biologically inspired models that are powerful tools for extracting texture, segments, and edges from images. As these attributes form the foundations of most image processing tasks, the use of PCNNs facilitates traditional tasks such as recognition, foveation, and image fusion. PCNN technology has also paved the way for new image processing techniques such as object isolation, spiral image fusion, image signatures, and content-based image searches. This volume contains examples of several image processing applications, as well as a review of hardware implementations.