__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 Content: Front matter,Copyright,List of Contributors,About the editors,PrefaceEntitled to full textChapter 1 - Bio-inspired computation and its applications in image processing: an overview, Pages 1-24 Chapter 2 - Fine-tuning enhanced probabilistic neural networks using metaheuristic-driven optimization, Pages 25-45 Chapter 3 - Fine-tuning deep belief networks using cuckoo search, Pages 47-59 Chapter 4 - Improved weighted thresholded histogram equalization algorithm for digital image contrast enhancement using the bat algorithm, Pages 61-86 Chapter 5 - Ground-glass opacity nodules detection and segmentation using the snake model, Pages 87-104 Chapter 6 - Mobile object tracking using the modified cuckoo search, Pages 105-130 Chapter 7 - Toward optimal watermarking of grayscale images using the multiple scaling factor–based cuckoo search technique, Pages 131-155 Chapter 8 - Bat algorithm–based automatic clustering method and its application in image processing, Pages 157-185 Chapter 9 - Multitemporal remote sensing image classification by nature- inspired techniques, Pages 187-219 Chapter 10 - Firefly algorithm for optimized nonrigid demons registration, Pages 221-237 Chapter 11 - Minimizing the mode-change latency in real-time image processing applications, Pages 239-268 Chapter 12 - Learning OWA filters parameters for SAR imagery with multiple polarizations, Pages 269-284 Chapter 13 - Oil reservoir quality assisted by machine learning and evolutionary computation, Pages 285-310 Chapter 14 - Solving imbalanced dataset problems for high-dimensional image processing by swarm optimization, Pages 311-321 Chapter 15 - Retinal image vasculature analysis software (RIVAS), Pages 323-345 Index, Pages 347-353 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. Key Features 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 Readership 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
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
