Application of an Enhanced Clustering Algorithm in Image Processing

In this paper, we introduce an enhanced clustering algorithm and explore its application in image processing. The algorithm facilitates effective edge detection and image segmentation, enabling deeper understanding of image content and structural characteristics. We provide accompanying program images and research documentation that detail the algorithm's implementation through optimized clustering functions, distance metric calculations, and iterative refinement processes. Key components include centroid initialization methods, pixel feature extraction techniques, and cluster assignment optimizations specifically designed for image data. Readers will learn practical approaches for applying clustering algorithms to image analysis tasks, including code structures for handling pixel intensity values, spatial relationships, and segmentation boundary refinement. The included materials demonstrate real-world implementation strategies suitable for integrating this methodology into actual image processing workflows.