Advances in Soft Computing and Machine Learning in Image Processing

ISBN-13: 9783319876276 / Angielski / Miękka / 2018 / 718 str.

Aboul Ella Hassanien; Diego Alberto Oliva

Recenzje

"Every chapter is well written and comprehensive. New algorithms are presented with experimental results and performance comparisons. The reference materials used are clearly cited. Overall, this well-edited volume consists of rich, highly useful, and relevant material. It will be useful for research students working in soft computing, machine vision, and image processing fields." (S. Ramakrishnan, Computing Reviews, July, 2018)

Spis treści

Color Spaces Advantages and Disadvantages in Image Color Clustering Segmentation.- Multi-objective Whale Optimization Algorithm for Multi-level Thresholding Segmentation.- Evaluating Swarm Optimization Algorithms for Segmentation of Liver Images.- Thermal Image Segmentation Using Evolutionary Computation Techniques.- News Videos Segmentation Using Dominant Colors Representation.

Blurb

This book is a collection of the latest applications of methods from soft computing and machine learning in image processing. It explores different areas ranging from image segmentation to the object recognition using complex approaches, and includes the theory of the methodologies used to provide an overview of the application of these tools in image processing.

The material has been compiled from a scientific perspective, and the book is primarily intended for undergraduate and postgraduate science, engineering, and computational mathematics students.  It can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence, and is a valuable resource for researchers in the evolutionary computation, artificial intelligence and image processing communities.