Program for JPG Image Segmentation Using Genetic Algorithm-Based Otsu's Method

This program provides source code implementation for JPG image segmentation using a genetic algorithm-enhanced Otsu's method. The algorithm optimizes pixel clustering by maximizing inter-class variance, effectively partitioning images into distinct regions. Through genetic algorithm optimization, the program automatically adjusts segmentation parameters and identifies optimal threshold values for image separation. Key implementation features include population initialization, fitness evaluation using Otsu's variance criterion, genetic operators (selection, crossover, mutation), and convergence monitoring. This solution is suitable for image processing applications, computer vision research, and related experimental studies where adaptive thresholding and automated parameter optimization are required. The code handles JPG format images and includes preprocessing steps for grayscale conversion and histogram calculation to support the genetic optimization process.