# Advances in Image Analysis Nature Inspired Methodology-proceedings on image processing-2011-free

Diffusing Vector Field of Gray-Scale Images for Image Segmentation 1

Xiaodong Zhuang, Nikos E. Mastorakis

The Curling Vector Field Transform of Gray-Scale Images: A Magneto-Static Inspired

Approach

9

Xiaodong Zhuang, Nikos E. Mastorakis

Region Shrinking and Image Segmentation based on the Compressing Vector Field 17

Xiaodong Zhuang, Nikos E. Mastorakis

A Novel Field-Source Reverse Transform for Image Structure Representation and Analysis 25

Xiaodong Zhuang, Nikos E. Mastorakis

A Magneto-Statics Inspired Transform for Structure Representation and Analysis of Digital

Images

37

Xiaodong Zhuang, Nikos E. Mastorakis

The Relative Potential Field as a Novel Physics-Inspired Method for Image Analysis 47

Xiaodong Zhuang, Nikos E. Mastorakis

Image Analysis based on the Discrete Magnetic Field Generated by the Virtual Edge Current in

Digital Images

60

Xiaodong Zhuang, Nikos E. Mastorakis

The Local Fuzzy Fractal Dimension as a Feature of Local Complexity for Digital Images and

Signals

75

Xiaodong Zhuang, Nikos E. Mastorakis

Image Processing with the Artificial Swarm Intelligence 86

Xiaodong Zhuang, Nikos E. Mastorakis

The Scale-Rate as the Measurement of Local Spatial and Temporal Complexity in Medical

Images

95

Xiaodong Zhuang, Nikos E. Mastorakis

A Physics-Inspired Model of Image Structure Representation by Deformable Elastic Grid 101

Xiaodong Zhuang, Nikos E. Mastorakis

Task Allocation in Multi-Agent Systems with Swarm Intelligence of Social Insects 107

Xiaodong Zhuang, Nikos E. Mastorakis

The development of human society relies on natural resources in every area (both material and spiritual). Nature has enormous power and intelligence behind its common daily appearance, and it is generous. We learn in it and from it, virtually as part of it. Nature-inspired systems and methods have a long history in human science and technology. For example, in the area of ** Modern heuristic techniques for combinatorial problems. Advanced topics in computer science **

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The software world is one of great richness and diversity. Many thousands of software products are available to users today, providing a wide variety of information and services in a wide variety of domains. While most of these programs provide their users with significant Classical complexity theory analyzes and classifies problems by the amount of a resource, usually time or space, that is required by algorithms solving them. It was a fundamental idea, going back to the work of Hartmanis and Stearns in the early 1960s, to measure the required

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, the recent well-known ones include the artificial neural network, genetic algorithm and swarm intelligence, which solve hard problems by imitating mechanisms in nature. Nature-inspired methods are also being quickly developed and applied in other areas. In this book volume, the authors just try to pick up a drop from the sea of nature’s intelligence, and apply it in a specific area. The authors hope that it may inspire the readers’ interest of nature’s intelligence when exploring in their own areas of science and technology. The research work in this book volume is supported by WSEAS on the topic of “Advanced Image Processing Techniques”. Nature-inspired methods in image processing have attracted more and more attention and research efforts. Related work has achieved promising results in practical tasks, which indicate that it is a direction potentially leading to breakthroughs of new image analysis techniques. Methods inspired by physical electro-magnetic field make up a main branch of this direction, which have been successfully applied in the practical applications including: recognition of human ear, face and gait; extraction of corner, edge, and shape skeleton in images. The existing methods inspired by electro-magnetic theory generally belong to two categories: analysis of the virtual field generated by the image (such as the “force field transform”), and deforming a shape or curve under the virtual force field generated by the image (such as the “active counter model”)

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