Bio-Inspired Computation and Applications in Image Processing summarizes the latest developments in bio-inspired computation in image processing, focusing on nature-inspired algorithms that are linked with deep learning, such as ant colony optimization, particle swarm optimization, and bat and firefly algorithms that have recently emerged in the field.
In addition to documenting state-of-the-art developments, this book also discusses future research trends in bio-inspired computation, helping researchers establish new research avenues to pursue.
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Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader in Modelling and Simulation at Middlesex University London, Fellow of the Institute of Mathematics and its Application (IMA) and a Book Series Co-Editor of the Springer Tracts in Nature-Inspired Computing. He has published more than 25 books and more than 400 peer-reviewed research publications with over 82000 citations, and he has been on the prestigious list of highly cited researchers (Web of Sciences) for seven consecutive years (2016-2022).
Joao Paulo Papa obtained his Ph.D. in Computer Science from University of Campinas, Brazil, in 2008, and was a visiting scholar at Harvard University from 2014-2015. He has been a Professor at Sao Paulo State University (UNESP), Brazil, since 2009, and his main interests include image processing, machine learning and meta-heuristic optimization.
This book summarizes the latest developments of bio-inspired computation in image processing, with a focus on nature-inspired algorithms linked with deep learning, such as ant colony optimization, particle swarm optimization, cuckoo search, bat algorithm and firefly algorithms, which have recently emerged in the field.
In addition to documenting state-of-the-art developments, this book also discusses future research trends in bio-inspired computation, helping researchers establish possible new research avenues to pursue
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