Color Image Segmentation Using Watershed-Based Automatic Seed Selection for Region Growing

This paper presents a color image segmentation method based on region growing with watershed-based automatic seed selection. This approach effectively partitions color images into distinct regions by leveraging watershed transformation to automatically generate optimal seed points for region growth algorithms. The implementation typically involves: applying watershed transformation to gradient magnitude images to identify potential region boundaries, selecting seeds from catchment basins with minimum regional intensity values, and growing regions using color similarity metrics (e.g., Euclidean distance in RGB/HSV space). Key functions in implementations often include: - Gradient calculation using Sobel/Prewitt operators - Watershed transform with marker-controlled flooding - Region growing with dynamic threshold adjustment - Color space conversion routines (RGB to HSV/LAB) This method enables superior understanding of image components and enhances image analysis capabilities. It finds applications in computer vision systems, medical imaging diagnostics, geological exploration, and other domains requiring precise image partitioning. The automated seed selection mechanism reduces manual intervention while maintaining segmentation accuracy through iterative region merging criteria.