Adaptive Image Processing: A Computational Intelligence Perspective

Adaptive Image Processing: A Computational Intelligence Perspective

by Ling Guan, Stuart William Perry, Hau San Wong
     
 

Adaptive image processing is one of the most important techniques in visual information processing, especially in early vision such as image restoration, filtering, enhancement, and segmentation. While existing books present some important aspects of the issue, there is not a single book that treats this problem from a viewpoint that is directly linked to human

Overview

Adaptive image processing is one of the most important techniques in visual information processing, especially in early vision such as image restoration, filtering, enhancement, and segmentation. While existing books present some important aspects of the issue, there is not a single book that treats this problem from a viewpoint that is directly linked to human perception - until now.

This reference treats adaptive image processing from a computational intelligence viewpoint, systematically and successfully, from theory to applications, using the synergies of neural networks, fuzzy logic, and evolutionary computation. Based on the fundamentals of human perception, this book gives a detailed account of computational intelligence methods and algorithms for adaptive image processing in regularization, edge detection, and early vision.

Adaptive Image Processing: A Computational Intelligence Perspective consists of 8 chapters:

Chapter 1 - Provides material of an introductory nature to describe the basic concepts and current state-of-the-art in the field of computational intelligence for image restoration and edge detection Chapter 2 - Gives a mathematical description of the restoration problem from the neural network perspective, and describes current algorithms based on this method Chapter 3 - Extends the algorithm presented in chapter 2 to implement adaptive constraint restoration methods for both spatially invariant and spatially variant degradations Chapter 4 - Utilizes a perceptually motivated image error measure to introduce novel restoration algorithms Chapter 5 - Examines how model-based neural networks can be used to solve image restoration problems Chapter 6 - Probes image restoration algorithms, making use of the principles of evolutionary computation Chapter 7 - Explores the difficult concept of image restoration when insufficient knowledge of the degrading function is available Chapter 8 - Studies the subject of edge detection and characterization using model-based neural networks

The first to treat adaptive image processing from a computational intelligence perspective, this work provides an excellent reference in R&D practice to researchers and IT technologists, is most suitable for teaching image processing and applied neural network courses, and will be of equal value for technical managers and executives in industries where intelligent visual information processing is required.

Editorial Reviews

Efforts to mimic biological vision currently focus on developing an effective adaptive image processing system for machine vision systems. This book presents the authors' research in computational intelligence that addresses the problems of image restoration and edge detection. They propose neural network techniques to perform segmentation, a fuzzy set theory to solve the requirement of characterization, and evolutionary computation for optimization. The final chapter describes a model-based feature detection neural network with hierarchical architecture which directly learns the essential characteristics of user-specified features through a training process. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Product Details

ISBN-13:
9780849302831
Publisher:
Taylor & Francis
Publication date:
12/21/2001
Series:
Image Processing Series
Pages:
288
Product dimensions:
6.40(w) x 9.50(h) x 0.88(d)

Customer Reviews

Average Review:

Write a Review

and post it to your social network

     

Most Helpful Customer Reviews

See all customer reviews >