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The use of metaheuristic algorithms (MA) has been increasing in
recent years, and the image processing field is not the exempted of
their application. In the last two years a big amount of MA has
been introduced as alternatives for solving complex optimization
problems. This book collects the most prominent MA of the 2019 and
2020 and verifies its use in image processing tasks. In addition,
literature review of both MA and digital image processing is
presented as part of the introductory information. Each algorithm
is detailed explained with special focus in the tuning parameters
and the proper implementation for the image processing tasks.
Besides several examples permits to the reader explore and confirm
the use of this kind of intelligent methods. Since image processing
is widely used in different domains, this book considers different
kinds of datasets that includes, magnetic resonance images, thermal
images, agriculture images, among others. The reader then can have
some ideas of implementation that complement the theory exposed of
each optimization mechanism. Regarding the image processing
problems this book consider the segmentation by using different
metrics based on entropies or variances. In the same way, the
identification of different shapes and the detection of objects are
also covered in the corresponding chapters. Each chapter is
complemented with a wide range of experiments and statistical
analysis that permits the reader to judge about the performance of
the MA. Finally, there is included a section that includes some
discussion and conclusions. This section also provides some open
questions and research opportunities for the audience.
This book is a collection of the most recent approaches that
combine metaheuristics and machine learning. Some of the methods
considered in this book are evolutionary, swarm, machine learning,
and deep learning. The chapters were classified based on the
content; then, the sections are thematic. Different applications
and implementations are included; in this sense, the book provides
theory and practical content with novel machine learning and
metaheuristic algorithms. The chapters were compiled using a
scientific perspective. Accordingly, the book is primarily intended
for undergraduate and postgraduate students of Science,
Engineering, and Computational Mathematics and is useful in courses
on Artificial Intelligence, Advanced Machine Learning, among
others. Likewise, the book is useful for research from the
evolutionary computation, artificial intelligence, and image
processing communities.
This book presents a collection of the most recent hybrid methods
for image processing. The algorithms included consider
evolutionary, swarm, machine learning and deep learning. The
respective chapters explore different areas of image processing,
from image segmentation to the recognition of objects using complex
approaches and medical applications. The book also discusses the
theory of the methodologies used to provide an overview of the
applications of these tools in image processing. The book is
primarily intended for undergraduate and postgraduate students of
science, engineering and computational mathematics, and can also be
used for courses on artificial intelligence, advanced image
processing, and computational intelligence. Further, it is a
valuable resource for researchers from the evolutionary
computation, artificial intelligence and image processing
communities.
This book is a collection of the most recent approaches that
combine metaheuristics and machine learning. Some of the methods
considered in this book are evolutionary, swarm, machine learning,
and deep learning. The chapters were classified based on the
content; then, the sections are thematic. Different applications
and implementations are included; in this sense, the book provides
theory and practical content with novel machine learning and
metaheuristic algorithms. The chapters were compiled using a
scientific perspective. Accordingly, the book is primarily intended
for undergraduate and postgraduate students of Science,
Engineering, and Computational Mathematics and is useful in courses
on Artificial Intelligence, Advanced Machine Learning, among
others. Likewise, the book is useful for research from the
evolutionary computation, artificial intelligence, and image
processing communities.
This book presents a study of the most important methods of image
segmentation and how they are extended and improved using
metaheuristic algorithms. The segmentation approaches selected have
been extensively applied to the task of segmentation (especially in
thresholding), and have also been implemented using various
metaheuristics and hybridization techniques leading to a broader
understanding of how image segmentation problems can be solved from
an optimization perspective. The field of image processing is
constantly changing due to the extensive integration of cameras in
devices; for example, smart phones and cars now have embedded
cameras. The images have to be accurately analyzed, and crucial
pre-processing steps, like image segmentation, and artificial
intelligence, including metaheuristics, are applied in the
automatic analysis of digital images. Metaheuristic algorithms have
also been used in various fields of science and technology as the
demand for new methods designed to solve complex optimization
problems increases. This didactic book is primarily intended for
undergraduate and postgraduate students of science, engineering,
and computational mathematics. It is also suitable for courses such
as artificial intelligence, advanced image processing, and
computational intelligence. The material is also useful for
researches in the fields of evolutionary computation, artificial
intelligence, and image processing.
This book presents a study of the most important methods of image
segmentation and how they are extended and improved using
metaheuristic algorithms. The segmentation approaches selected have
been extensively applied to the task of segmentation (especially in
thresholding), and have also been implemented using various
metaheuristics and hybridization techniques leading to a broader
understanding of how image segmentation problems can be solved from
an optimization perspective. The field of image processing is
constantly changing due to the extensive integration of cameras in
devices; for example, smart phones and cars now have embedded
cameras. The images have to be accurately analyzed, and crucial
pre-processing steps, like image segmentation, and artificial
intelligence, including metaheuristics, are applied in the
automatic analysis of digital images. Metaheuristic algorithms have
also been used in various fields of science and technology as the
demand for new methods designed to solve complex optimization
problems increases. This didactic book is primarily intended for
undergraduate and postgraduate students of science, engineering,
and computational mathematics. It is also suitable for courses such
as artificial intelligence, advanced image processing, and
computational intelligence. The material is also useful for
researches in the fields of evolutionary computation, artificial
intelligence, and image processing.
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