Mean Shift Image Segmentation Test Program

This document provides detailed documentation for the Mean Shift Image Segmentation Test Program, which employs the meanshift algorithm for clustering-based segmentation of color images. The implementation demonstrates excellent segmentation performance while reporting computational time and the number of identified clusters. Key technical components include bidirectional conversion between RGB and LUV color spaces (using transformation matrices for color space mapping), reduction of image matrix data to lower-dimensional arrays (via pixel feature vector extraction), and optimized clustering routines. The codebase contains extensive inline comments explaining algorithm parameters like bandwidth selection and convergence criteria, along with performance-optimized functions for histogram processing and modal seeking. Sample output images validate the segmentation quality across different image types. This resource serves as an excellent reference for practitioners in computer vision, machine learning, and pattern recognition, particularly for understanding practical implementation of mean shift clustering with color space considerations. Supplementary implementation details cover kernel function configuration and cluster merging techniques for post-processing.