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.
- Reviews the latest developments in bio-inspired computation in image processing
- Focuses on the introduction and analysis of the key bio-inspired methods and techniques
- Combines theory with real-world applications in image processing
- Helps solve complex problems in image and signal processing
- Contains a diverse range of self-contained case studies in real-world applications
Graduates and PhD students and lecturers in electronic engineering, image processing, signal processing, data science and applied science. Researchers and engineers as well as experienced experts
Chapter 1. Bio-Inspired Computation and its Applications in Image Processing: An Overview
Chapter 2. Fine-Tuning Enhanced Probabilistic Neural Networks Using Meta-heuristic-driven Optimization
Chapter 3. Fine-Tuning Deep Belief Networks using Cuckoo Search
Chapter 4. Improved Weighted Thresholded Histogram Equalization Algorithm for Digital Image Contrast Enhancement Using Bat Algorithm
Chapter 5. Ground Glass Opacity Nodules Detection and Segmentation using Snake Model
Chapter 6. Mobile Object Tracking Using Cuckoo Search
Chapter 7. Towards Optimal Watermarking of Grayscale Images Using Multiple Scaling Factor based Cuckoo Search Technique
Chapter 8. Bat algorithm based automatic clustering method and its application in image processing
Chapter 9. Multi-temporal remote sensing image registration by nature inspired techniques
Chapter 10. Firefly Algorithm for Optimized Non-Rigid Demons Registration
Chapter 11. Minimizing the Mode-Change Latency in Real-Time Image Processing Applications
Chapter 12. Learning OWA Filters parameters for SAR Imagery with multiple polarizations
Chapter 13. Oil Reservoir Quality Assisted by Machine learning and Evolutionary Computation
Chapter 14. Solving Imbalanced Dataset Problems for High Dimensional Image Processing by Swarm Optimization
Chapter 15. Rivas: The Automated Retinal Image analysis Software
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- © Academic Press 2016
- 5th August 2016
- Academic Press
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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 at Middlesex University London, Adjunct Professor at Reykjavik University (Iceland) and Guest Professor at Xi’an Polytechnic University (China). He is an elected Bye-Fellow at Downing College, Cambridge University. He is also the IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management, and the Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO).
School of Science and Technology, Middlesex University, UK
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.
Assistant professor, Sao Paulo State University (UNESP), Brazil; Visiting scholar, Harvard University, Cambridge, MA, USA