Image Segmentation Using Threshold Method with Genetic Algorithm
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Resource Overview
Self-developed implementation featuring operational code with simplified structure, ideal for beginners learning genetic algorithm applications in image segmentation
Detailed Documentation
This documentation presents my original implementation of an image segmentation system utilizing threshold methods optimized by genetic algorithms. The code is fully functional and designed with simplicity in mind, making it particularly suitable for beginners exploring digital image processing techniques. The implementation demonstrates key concepts including population initialization, fitness evaluation based on segmentation quality metrics, and iterative genetic operations (selection, crossover, mutation) to evolve optimal threshold values. Core functions handle grayscale image processing, threshold application, and objective function calculation to maximize inter-class variance while minimizing intra-class variance.
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