Minimum Cross-Entropy Image Segmentation Source Code (Original Implementation)

The minimum cross-entropy algorithm for image segmentation is a widely-used method in digital image processing. The core principle of this algorithm is to determine image boundaries by analyzing pixel differences within the image. It achieves segmentation by calculating similarity measures between pixels. Key implementation aspects include: - Pixel intensity distribution analysis using histogram-based thresholding - Cross-entropy minimization between foreground and background regions - Optimal threshold selection through iterative optimization The provided source code represents an original implementation featuring: - Efficient matrix operations for pixel similarity computation - Adaptive threshold calculation algorithms - Modular structure for easy integration and customization - Comprehensive code comments with mathematical derivations Related academic references and technical notes are embedded within the source code documentation for further study and verification.