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.
1. Cover - p. i 2. Title page - p. iii 3. Table of Contents - p. iii 4. Copyright - p. ii 5. List of Contributors - p. xiii 6. About the editors - p. xv 7. Preface - p. xvii 8. Chapter 1: Bio-inspired computation and its applications in image processing: an overview - p. xx 9. Chapter 2: Fine-tuning enhanced probabilistic neural networks using metaheuristic-driven optimization - p. 24 10. Chapter 3: Fine-tuning deep belief networks using cuckoo search - p. 45 11. Chapter 4: Improved weighted thresholded histogram equalization algorithm for digital image contrast enhancement using the bat algorithm - p. 59 12. Chapter 5: Ground-glass opacity nodules detection and segmentation using the snake model - p. 86 13. Chapter 6: Mobile object tracking using the modified cuckoo search - p. 104 14. Chapter 7: Toward optimal watermarking of grayscale images using the multiple scaling factor–based cuckoo search technique - p. 130 15. Chapter 8: Bat algorithm–based automatic clustering method and its application in image processing - p. 155 16. Chapter 9: Multitemporal remote sensing image classification by nature- inspired techniques - p. 185 17. Chapter 10: Firefly algorithm for optimized nonrigid demons registration - p. 219 18. Chapter 11: Minimizing the mode-change latency in real-time image processing applications - p. 237 19. Chapter 12: Learning OWA filters parameters for SAR imagery with multiple polarizations - p. 268 20. Chapter 13: Oil reservoir quality assisted by machine learning and evolutionary computation - p. 284 21. Chapter 14: Solving imbalanced dataset problems for high-dimensional image processing by swarm optimization - p. 310 22. Chapter 15: Retinal image vasculature analysis software (RIVAS) - p. 321 23. Index - p. 345
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.
1. Cover - p. i 2. Title page - p. iii 3. Table of Contents - p. iii 4. Copyright - p. ii 5. List of Contributors - p. xiii 6. About the editors - p. xv 7. Preface - p. xvii 8. Chapter 1: Bio-inspired computation and its applications in image processing: an overview - p. xx 9. Chapter 2: Fine-tuning enhanced probabilistic neural networks using metaheuristic-driven optimization - p. 24 10. Chapter 3: Fine-tuning deep belief networks using cuckoo search - p. 45 11. Chapter 4: Improved weighted thresholded histogram equalization algorithm for digital image contrast enhancement using the bat algorithm - p. 59 12. Chapter 5: Ground-glass opacity nodules detection and segmentation using the snake model - p. 86 13. Chapter 6: Mobile object tracking using the modified cuckoo search - p. 104 14. Chapter 7: Toward optimal watermarking of grayscale images using the multiple scaling factor–based cuckoo search technique - p. 130 15. Chapter 8: Bat algorithm–based automatic clustering method and its application in image processing - p. 155 16. Chapter 9: Multitemporal remote sensing image classification by nature- inspired techniques - p. 185 17. Chapter 10: Firefly algorithm for optimized nonrigid demons registration - p. 219 18. Chapter 11: Minimizing the mode-change latency in real-time image processing applications - p. 237 19. Chapter 12: Learning OWA filters parameters for SAR imagery with multiple polarizations - p. 268 20. Chapter 13: Oil reservoir quality assisted by machine learning and evolutionary computation - p. 284 21. Chapter 14: Solving imbalanced dataset problems for high-dimensional image processing by swarm optimization - p. 310 22. Chapter 15: Retinal image vasculature analysis software (RIVAS) - p. 321 23. Index - p. 345
