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Bio-Inspired Computation and Applications in Image Processing
Presents the latest developments in bio-inspired computation in image processing, with a focus on nature-inspired algorithms linked to deep learning
Xin-She Yang (Author), João Paulo Papa (Author)
9780128045367, Elsevier Science
Hardback, published 5 August 2016
374 pages
23.4 x 19 x 2.6 cm, 0.7 kg
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.
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
Subject Areas: Image processing [UYT], Graphics programming [UML], Digital photography: consumer/user guides [UDP], Special kinds of photography [AJR]
