Bio-Inspired Computation and Applications in Image Processing

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. 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 INDICE: Chapter 1. Bio-Inspired Computation and its Applications in Image Processing: An OverviewChapter 2. Fine-Tuning Enhanced Probabilistic Neural Networks Using Meta-heuristic-driven OptimizationChapter 3. Fine-Tuning Deep Belief Networks using Cuckoo SearchChapter 4. Improved Weighted Thresholded Histogram Equalization Algorithm for Digital Image Contrast Enhancement Using Bat AlgorithmChapter 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 processingChapter 9. Multi-temporal remote sensing image registration by nature inspired techniques Chapter 10. Firefly Algorithm for Optimized Non-Rigid Demons RegistrationChapter 11. Minimizing the Mode-Change Latency in Real-Time Image Processing ApplicationsChapter 12. Learning OWA Filters parameters for SAR Imagery with multiple polarizationsChapter 13. Oil Reservoir Quality Assisted by Machine learning and Evolutionary Computation Chapter 14. Solving Imbalanced Dataset Problems for High Dimensional Image Processing by Swarm OptimizationChapter 15. Rivas: The Automated Retinal Image analysis Software

• ISBN: 978-0-12-804536-7
• Editorial: Academic Press
• Encuadernacion: Cartoné
• Páginas: 500
• Fecha Publicación: 01/09/2016
• Nº Volúmenes: 1
• Idioma: Inglés