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 highlights the relevance of related application areas for advanced as well as novice-user application. The book presents core concepts, methodological aspects, and advanced feature opportunities, focusing on major, real-time applications in engineering and health science. It will appeal to researchers, data scientists, industry professionals, and graduate students. 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.

Inhalt

Part 1: Intelligent Fractal-Based Image Analysis Introduction to Intelligent Fractal-Based Image Analysis - Editors 1.1 Insights into Intelligent Fractal-Based Image Analysis with Pattern Recognition 1.2 Analysis of Mandelbrot Set Fractal Images Using Machine Learning Based Approach 1.3 Chaos-based Image Encryption1.4 Fractal Feature-based Image Classification Part 2: Recognition Model Using Fractal Features 2.1 The study of Source Image and its Futuristic Quantum Applications: An insight from Fractal Analysis 2.2 Wavelet Multifractal Characterization of Anisotropic Oscillating Singularities and Application in Nanomaterials 2.3 GID-Net: Generic Image Denoising using Convolutional Auto-encoders 2.4 Geometrical Description of Image Analysis Using Fractal Theory Part 3: Fractals in Disease Identification and Control 3.1 Fractal Theory and the Explainable Artificial Intelligence of Cancer Medical Imaging 3.2 Computational Complexity of Multifractal Models-based MRI Image Processing for Subgroups of Multiple Sclerosis Patients' Diagnosis and Course in Precision Medicine 3.3 AI-Stochastic Fractal Analysis of the Alzheimer disease (AD) Medical Images 3.4 Preliminary Study of Retinal Lesions Classification on Rational Fundus Images for the Diagnosis of Retinal Diseases