Image Segmentation Using Graph Cut Methods

In this documentation, I am sharing a program code for image segmentation based on graph cut methods, which I hope will be beneficial for your work! Graph cut methodology represents a fundamental computer vision technique used to partition images into distinct regions or objects. This implementation employs max-flow/min-cut algorithms to optimize energy functions comprising regional and boundary terms. The code structure includes key components for graph construction, capacity assignment between pixels, and optimal cut computation using Boykov-Kolmogorov algorithms. Through this program, you can learn practical implementation of image segmentation and customize it according to your specific requirements. The code has been meticulously designed and optimized to deliver efficient and accurate segmentation results, featuring preprocessing routines for handling color spaces and post-processing modules for region refinement. Whether you are a professional in computer vision or a beginner, this code provides comprehensive insights into both theoretical principles and practical applications of image segmentation. The implementation supports both interactive seed-based segmentation and automatic threshold-based approaches. Should you have any questions or suggestions regarding this code, please feel free to contact me at any time!