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Bio-inspired computation and applications in image processing /
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 firef...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London, United Kingdom :
Academic Press : Elsevier,
2016.
�20 |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 020 | |z 9780128045367 | ||
| 020 | |z 0128045361 | ||
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| 245 | 0 | 0 | |a Bio-inspired computation and applications in image processing / |c edited by Xin-She Yang, Jo�ao Paulo Papa. |
| 260 | |a London, United Kingdom : |b Academic Press : |b Elsevier, |c 2016. | ||
| 264 | 4 | |c �20 | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover ; Title page; Copyright page; Contents; List of Contributors; About the editors; Preface; Chapter 1 -- Bio-inspired computation and its applications in image processing: an overview ; 1 -- Introduction; 2 -- Image processing and optimization; 2.1 -- Image segmentation via optimization; 2.2 -- Optimization; 3 -- Some key issues in optimization; 3.1 -- Efficiency of an algorithm; 3.2 -- How to choose algorithms?; 3.3 -- Time and resource constraints; 4 -- Nature-inspired optimization algorithms; 4.1 -- Bio-inspired algorithms based on swarm intelligence; 4.1.1 -- Ant and bee algorithms. | |
| 505 | 8 | |a 4.1.2 -- Bat algorithm4.1.3 -- Particle swarm optimization; 4.1.4 -- Firefly algorithm; 4.1.5 -- Cuckoo search; 4.1.6 -- Flower pollination algorithm; 4.2 -- Nature-inspired algorithms not based on�swarm�intelligence; 4.2.1 -- Simulated annealing; 4.2.2 -- Genetic algorithms; 4.2.3 -- Differential evolution; 4.2.4 -- Harmony search; 4.3 -- Other algorithms; 5 -- Artificial neural networks and support vector machines; 5.1 -- Artificial neural networks; 5.2 -- Support vector machines; 6 -- Recent trends and applications; 7 -- Conclusions; References. | |
| 505 | 8 | |a Chapter 2 -- Fine-tuning enhanced probabilistic neural networks using metaheuristic-driven optimization 1 -- Introduction; 2 -- Probabilistic neural network; 2.1 -- Theoretical foundation; 2.2 -- Enhanced probabilistic neural network with local decision circles; 3 -- Methodology and experimental results; 3.1 -- Datasets; 3.2 -- Experimental setup; 3.2.1 -- PNNs versus EPNNs; 3.2.2 -- Evaluating the EPNN and metaheuristic-based EPNNs; 4 -- Conclusions; Acknowledgments; References; Chapter 3 -- Fine-tuning deep belief networks using cuckoo search ; 1 -- Introduction; 2 -- Theoretical background. | |
| 505 | 8 | |a 2.1 -- Deep belief networks2.1.1 -- Restricted Boltzmann machines; 2.1.2 -- Learning algorithm; 2.2 -- Deep belief nets; 2.3 -- Cuckoo search; 3 -- Methodology; 3.1 -- Datasets; 3.2 -- Harmony search and particle swarm optimization; 4 -- Experiments and results; 4.1 -- Experimental setup; 4.2 -- Experimental results; 5 -- Conclusions; Acknowledgments; References; Chapter 4 -- Improved weighted thresholded histogram equalization algorithm for digital image contrast enhancement using ... ; 1 -- Introduction; 2 -- Literature review; 3 -- Bat algorithm; 4 -- Our proposed method. | |
| 505 | 8 | |a 4.1 -- Global histogram equalization4.2 -- Development of weighting constraints with respect to the threshold; 4.3 -- Optimizing the weighting constraints using the bat algorithm; 5 -- Experimental results; 6 -- Conclusions; Acknowledgment; References; Chapter 5 -- Ground-glass opacity nodules detection and segmentation using the snake model ; 1 -- Introduction; 2 -- Related works on delineation of GGO lesions; 3 -- Snake model; 3.1 -- Background; 3.2 -- Basic formulation; 3.3 -- Variants of snake models; 4 -- Proposed framework; 4.1 -- Overall framework; 4.2 -- Experimental data; 5 -- Result and discussion. | |
| 500 | |a Includes index. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a 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. | ||
| 650 | 0 | |a Natural computation. | |
| 650 | 0 | |a Image processing |x Digital techniques. | |
| 650 | 6 | |a Calcul naturel. |0 (CaQQLa)000265307 | |
| 650 | 6 | |a Traitement d'images |x Techniques num�eriques. |0 (CaQQLa)201-0118646 | |
| 650 | 7 | |a digital imaging. |2 aat |0 (CStmoGRI)aat300237903 | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Image processing |x Digital techniques |2 fast |0 (OCoLC)fst00967508 | |
| 650 | 7 | |a Natural computation |2 fast |0 (OCoLC)fst01745866 | |
| 700 | 1 | |a Yang, Xin-She. | |
| 700 | 1 | |a Papa, Jo�ao Paulo. | |
| 776 | 0 | 8 | |i Print version : |z 9780128045367 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128045367 |z Texto completo |