Adaptive Image Processing: A Computational Intelligence Perspective, Second Edition (Image Processing Series)

Illustrating essential aspects of adaptive image processing from a computational intelligence viewpoint, the second edition of Adaptive Image Processing: A Computational Intelligence Perspective provides an authoritative and detailed account of computational intelligence (CI) methods and algorithms for adaptive image processing in regularization, edge detection, and early vision. With three new chapters and updated information throughout, the new edition of this popular reference includes substantial new material that focuses on applications of advanced CI techniques in image processing applications. It introduces new concepts and frameworks that demonstrate how neural networks, support vector machines, fuzzy logic, and evolutionary algorithms can be used to address new challenges in image processing, including low-level image processing, visual content analysis, feature extraction, and pattern recognition. Emphasizing developments in state-of-the-art CI techniques, such as content-based image retrieval, this book continues to provide educators, students, researchers, engineers, and technical managers in visual information processing with the up-to-date understanding required to address contemporary challenges in image content processing and analysis.

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.

illustrating Essential Aspects Of Adaptive Image Processing From A Computational Intelligence Viewpoint, The Second Edition Of adaptive Image Processing: A Computational Intelligence Perspective Provides An Authoritative And Detailed Account Of Computational Intelligence (ci) Methods And Algorithms For Adaptive Image Processing In Regularization, Edge Detection, And Early Vision.

with Three New Chapters And Updated Information Throughout, The New Edition Of This Popular Reference Includes Substantial New Material That Focuses On Applications Of Advanced Ci Techniques In Image Processing Applications. It Introduces New Concepts And Frameworks That Demonstrate How Neural Networks, Support Vector Machines, Fuzzy Logic, And Evolutionary Algorithms Can Be Used To Address New Challenges In Image Processing, Including Low-level Image Processing, Visual Content Analysis, Feature Extraction, And Pattern Recognition.

new To The Second Edition:

• a New Chapter On A Family Of Unsupervised Algorithms With A Basis In Self-organization Yet Somewhat Free From Many Of The Constraints Typical Of Other Well-known Self-organizing Architectures
• new Material On Recent Challenges In Image Content Analysis And Classification, Including Small Sample Problems And Fuzzy User Perception
• a New Technique In Visual Query Processing And Visualization In 2d Space
• new Experiments And Updates On Perceptual Error-based Restoration

emphasizing Developments In State-of-the-art Ci Techniques, Such As Content-based Image Retrieval, This Book Continues To Provide Educators, Students, Researchers, Engineers, And Technical Managers In Visual Information Processing With The Up-to-date Understanding Required To Address Contemporary Challenges In Image Content Processing And Analysis.

A complete index of CRC handbooks identifying specific volumes, titles and the page numbers necessary to locate the information required. It includes subjects, compounds and names of organisms from the fields of biomedical science, biology, chemistry, engineering and computer science and physics 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. This work focuses on applications of advanced CI techniques in image processing applications.