Watershed-Based Image Segmentation

This paper presents a watershed-based image segmentation methodology that includes detailed source code implementation, test images, and comprehensive experimental documentation. Our approach utilizes the watershed algorithm to partition images into distinct regions, enabling clearer differentiation between foreground objects and background elements. The implementation employs gradient magnitude computation and marker-controlled watershed transformation to prevent over-segmentation issues. Key functions include gradient calculation using Sobel operators, morphological operations for marker extraction, and watershed transformation with connectivity analysis. Experimental results demonstrate the method's effectiveness in handling complex boundaries and its accuracy in region segmentation. Additionally, we provide recommendations for algorithmic enhancements such as incorporating edge-preserving filters and adaptive marker selection, along with potential applications in medical imaging and remote sensing analysis to facilitate further exploration in this research domain.