Four Image Segmentation Algorithms Included in Compressed Archive

The compressed archive includes source code files implementing four fundamental image segmentation algorithms: threshold-based segmentation (using intensity thresholding for pixel classification), region growing (iteratively expanding regions from seed points based on similarity criteria), split-and-merge (dividing images into homogeneous regions using quad-tree decomposition), and K-means clustering (partitioning pixels into K clusters based on feature similarity). These algorithms provide practical tools for image processing tasks, enabling effective validation and analysis of segmentation results. The archive also contains detailed documentation explaining how to implement and apply these algorithms for image segmentation tasks, including parameter configuration guidelines and performance evaluation methods. The provided sample images can be used to test algorithm performance and verify segmentation accuracy. This resource is designed to assist researchers and developers in completing computer vision projects efficiently.