Fractals are infinite, complex patterns used in modeling physical and dynamic systems. Fractal theory research has increased across different fields of applications including engineering science, health science, and social science. Recent literature shows the vital role fractals play in digital image analysis, specifically in biomedical image processing. Fractal graphics is an interdisciplinary field that deals with how computers can be used to gain high-level understanding from digital images. Integrating artificial intelligence with fractal characteristics has resulted in new interdisciplinary research in the fields of pattern recognition and image processing analysis.
Intelligent Fractal-Based Image Analysis: Application in Pattern Recognition and Machine Vision provides insights into the current strengths and weaknesses of different applications as well as research findings on fractal graphics in engineering and science applications. The book aims to improve the exchange of ideas and coherence between various core computing methods and highlight the relevance of related application areas for advanced as well as novice-user application. The book presents an in-depth look at core concepts, methodological aspects, and advanced feature opportunities, focusing on major real time applications in engineering science and health science. The book will appeal to researchers, data scientists, industry professionals, and graduate students in the fields of fractal graphics and its related applications.
1. Intelligent Fractal-Based Image Analysis - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page v 7. Contributors - p. Page v 8. Foreword - p. Page vi 9. Preface - p. Page viii 10. Acknowledgments - p. Page xvii 11. List of Illustrations - p. Page xix 12. List of Tables - p. Page xx 13. Chapter 1 : A deep insight into intelligent fractal-based image analysis with pattern recognition - p. Page xx 14. Chapter 2 : Analysis of Mandelbrot set fractal images using a machine learning based approach - p. Page 26 15. Chapter 3 : Chaos-based image encryption - p. Page 42 16. Chapter 4 : Fractal feature based image classification - p. Page 68 17. Chapter 5 : The study of source image and its futuristic quantum applications: an insight from fractal analysis - p. Page 87 18. Chapter 6 : Deep CNNS and fractal-based sequence learning for violence detection in surveillance videos - p. Page 106 19. Chapter 7 : Wavelets for anisotropic oscillations in nanomaterials: Wavelets for anisotropic oscillations - p. Page 125 20. Chapter 8 : Comparative analysis of approaches to optimize fractal image compression - p. Page 162 21. Chapter 9 : Alzheimer disease (AD) medical image analysis with convolutional neural networks: AD-CNN - p. Page 187 22. Chapter 10 : An intelligent fractal-dimension-based model for brain-tumor MRI analysis - p. Page 224 23. Chapter 11 : Fractal dimension analysis using hybrid RDBC and IDBC for gray scale images - p. Page 246 24. Chapter 12 : Preliminary analysis and survey of retinal disease diagnosis through identification and segmentation of bright and dark lesions - p. Page 261 25. Index - p. Page 279
Fractals are infinite, complex patterns used in modeling physical and dynamic systems. Fractal theory research has increased across different fields of applications including engineering science, health science, and social science. Recent literature shows the vital role fractals play in digital image analysis, specifically in biomedical image processing. Fractal graphics is an interdisciplinary field that deals with how computers can be used to gain high-level understanding from digital images. Integrating artificial intelligence with fractal characteristics has resulted in new interdisciplinary research in the fields of pattern recognition and image processing analysis.
Intelligent Fractal-Based Image Analysis: Application in Pattern Recognition and Machine Vision provides insights into the current strengths and weaknesses of different applications as well as research findings on fractal graphics in engineering and science applications. The book aims to improve the exchange of ideas and coherence between various core computing methods and highlight the relevance of related application areas for advanced as well as novice-user application. The book presents an in-depth look at core concepts, methodological aspects, and advanced feature opportunities, focusing on major real time applications in engineering science and health science. The book will appeal to researchers, data scientists, industry professionals, and graduate students in the fields of fractal graphics and its related applications.
1. Intelligent Fractal-Based Image Analysis - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page v 7. Contributors - p. Page v 8. Foreword - p. Page vi 9. Preface - p. Page viii 10. Acknowledgments - p. Page xvii 11. List of Illustrations - p. Page xix 12. List of Tables - p. Page xx 13. Chapter 1 : A deep insight into intelligent fractal-based image analysis with pattern recognition - p. Page xx 14. Chapter 2 : Analysis of Mandelbrot set fractal images using a machine learning based approach - p. Page 26 15. Chapter 3 : Chaos-based image encryption - p. Page 42 16. Chapter 4 : Fractal feature based image classification - p. Page 68 17. Chapter 5 : The study of source image and its futuristic quantum applications: an insight from fractal analysis - p. Page 87 18. Chapter 6 : Deep CNNS and fractal-based sequence learning for violence detection in surveillance videos - p. Page 106 19. Chapter 7 : Wavelets for anisotropic oscillations in nanomaterials: Wavelets for anisotropic oscillations - p. Page 125 20. Chapter 8 : Comparative analysis of approaches to optimize fractal image compression - p. Page 162 21. Chapter 9 : Alzheimer disease (AD) medical image analysis with convolutional neural networks: AD-CNN - p. Page 187 22. Chapter 10 : An intelligent fractal-dimension-based model for brain-tumor MRI analysis - p. Page 224 23. Chapter 11 : Fractal dimension analysis using hybrid RDBC and IDBC for gray scale images - p. Page 246 24. Chapter 12 : Preliminary analysis and survey of retinal disease diagnosis through identification and segmentation of bright and dark lesions - p. Page 261 25. Index - p. Page 279
