Hybrid Metaheuristics for Image Analysis

This book presents contributions in the field of computational intelligence for the purpose of image analysis. The chapters discuss how problems such as image segmentation, edge detection, face recognition, feature extraction, and image contrast enhancement can be solved using techniques such as genetic algorithms and particle swarm optimization.

The contributions provide a multidimensional approach, and the book will be useful for researchers in computer science, electrical engineering, and information technology.

Current and Future Trends in Segmenting Satellite Images Using Hybrid and Dynamic Genetic Algorithms.- A Hybrid Metaheuristic Algorithm Based on Quantum Genetic Computing for Image Segmentation.- Genetic Algorithm Implementation to Optimize the Hybridization of Feature Extraction and Metaheuristic Classifiers.- Optimization of a HMM-Based Hand Gesture Recognition System Using a Hybrid Cuckoo Search Algorithm.- Satellite Image Contrast Enhancement Using Fuzzy Termite Colony Optimization.- Image Segmentation Using Metaheuristic-Based DeformableModels.- Hybridization of the Univariate Marginal Distribution Algorithm with Simulated Annealing for Parametric Parabola Detection.- Image Thresholding Based on Fuzzy Particle Swarm Optimization.- Hybrid Metaheuristics Applied to Image Reconstruction for an Electrical Impedance Tomography Prototype.