Advances and Applications of Optimised Algorithms in Image Processing

This book presents a study of the use of optimization algorithms in complex image processing problems. The problems selected explore areas ranging from the theory of image segmentation to the detection of complex objects in medical images. Furthermore, the concepts of machine learning and optimization are analyzed to provide an overview of the application of these tools in image processing.

The material has been compiled from a teaching perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, and can be used for courses on Artificial Intelligence, Advanced Image Processing, Computational Intelligence, etc. Likewise, the material can be useful for research from the evolutionary computation, artificial intelligence and image processing communities.

Provides explanations of the algorithms used, selected problems and implementations, as well as practical examples, comparisons and experimental results All chapters are self-contained and can all be read separately Offers a compendium of the most interesting electromagnetism applications, e.g. optimization (EMO)-based algorithms for image processing problems Guides the reader from the basics of optimization to different case studies Includes supplementary material: sn.pub/extras

Contenu
An introduction to machine learning.- Optimization.- Electromagnetism Like Optimization Algorithm: An Introduction.- Digital image segmentation as an optimization problem.- Template matching using a physical inspired algorithm.-Detection of circular shapes in digital images.- A medical application: Blood cell segmentation by circle detection.- An EMO Improvement: Opposition-Based Electromagnetism-Like for Global Optimization <p