QuadTree Segmentation for Color RGB Images

In this program, we implement image segmentation using the QuadTree method specifically designed for color RGB images. The algorithm recursively divides the image into four quadrants when a region doesn't meet homogeneity criteria, typically based on color variance thresholds. This approach allows us to partition images into smaller, more manageable regions, facilitating better understanding and processing of different image components. The implementation involves key functions for calculating regional color statistics, evaluating splitting conditions, and managing recursive subdivision. After considerable development effort, we successfully created a robust solution that handles RGB color space calculations while maintaining efficient memory management through tree-based data structures. The program effectively balances segmentation precision with computational efficiency by implementing adaptive thresholding and optimized region-merging techniques.