Nature and Biologically Inspired Image Segmentation Techniques

Abstract

Image processing is among the significant areas of growth in the current scenario. It consist of a set of techniques typically used to enhance the raw image obtained from different scenes. Segmentation of images is an essential step in image analysis and pre-processing. During the course of the work, standard multilevel thresholding methods are very effective due to their low computational cost, reliability, reduced convergence time, and precision. Nature-inspired methods of optimization play an essential role in the processing of images. Several optimization procedures have been proposed for different image processing applications. These optimization techniques can improve the performance of image segmentation, image restoration, edge detection, image enhancement, pattern recognition, image generation, image thresholding, and image fusion algorithms. This paper includes an overview of several metaheuristic firefly algorithm (FA), differential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), artificial bee colony optimization (ABC), etc. Moreover, artificial neural networks (ANN) and other machine learning techniques (nature or biological inspired) are discussed in context with image segmentation application and their algorithms.