Coin Segmentation Using K-Means and PSO Optimization

This Jupyter Notebook demonstrates the process of coin segmentation using an image as input.

Objective

The main objective of this project is to accurately segment coins in an image.

Methodology

1. K-Means Algorithm: The K-Means clustering algorithm is used to segment the coins from the background in the input image.
2. PSO Optimization: To refine the segmentation, PSO (Particle Swarm Optimization) is applied, improving the accuracy and quality of the segmented regions.

Summary

This project highlights the effectiveness of combining K-Means clustering with PSO optimization for image segmentation tasks. By using these techniques, we achieved more accurate and reliable segmentation of coins in the given images, using PSNR value as the measuring metric.