Nature Inspired Optimization Techniques for Image Processing Applications

This book provides a platform for exploring nature-inspired optimization techniques in the context of imaging applications. Optimization has become part and parcel of all computational vision applications, and since the amount of data used in these applications is vast, the need for optimization techniques has increased exponentially. These accuracy and complexity are a major area of concern when it comes to practical applications. However, these optimization techniques have not yet been fully explored in the context of imaging applications. By presenting interdisciplinary concepts, ranging from optimization to image processing, the book appeals to a broad readership, while also encouraging budding engineers to pursue and employ innovative nature-inspired techniques for image processing applications.

Covers most of the nature-inspired optimization algorithms in a single resource Provides step-by-step algorithm coverage to facilitate implementation for budding researchers Addresses specific application areas, helping researchers choose a specific optimization area for their application

Inhalt
Firefly Optimization Based Improved Fuzzy Clustering for CT/MR Image Segmentation.- Bat Optimization based Vector Quantization Algorithm for Medical Image Compression.- An Assertive Framework for Automatic Tamil Sign Language Recognition System using Computational Intelligence.- Improved detection of steganographic algorithms in spatial LSB stego images using hybrid GRASP-BGWO optimisation.- Nature inspired optimization techniques for Image Processing - A short review.- Application of Ant Colony Optimization for Enhancement of Visual Cryptography Images.- Plant phenotyping through Image analysis using nature inspired optimization techniques.- Cuckoo Optimization Algorithm (COA) for image processing.- Artificial Bee Colony Based Feature Selection for Automatic Skin Disease Identification of Mango Fruit.- Analyzing the Effect of Optimization Strategies in Deep Convolutional Neural Network.- A Novel Underwater Image Enhancement Approach with Wavelet Transform Supported by Differential Evolution Algorithm.- Feature Selection in Fetal Biometrics for Abnormality Detection in Ultrasound Images. <p