Graph Cuts Implementation for Image Segmentation

This code serves as a fundamental component for learning Graph Cuts, making it particularly suitable for beginners to study and experiment with. Graph Cuts represent a powerful tool for image segmentation with applications across various domains including medical image processing and computer vision. The implementation typically involves energy minimization using max-flow/min-cut algorithms, where the code demonstrates how to construct graph nodes representing image pixels and edges modeling regional and boundary properties. Through studying this code, beginners can gain deeper insights into Graph Cuts' core principles, including how to define data terms (regional constraints) and smoothness terms (boundary constraints) in the energy function. The code illustrates practical application scenarios, showing how to initialize graph structures, implement optimization routines, and handle different image segmentation challenges. Key functions often include graph construction, capacity assignment for edges, and min-cut computation using algorithms like Ford-Fulkerson or Boykov-Kolmogorov. Additionally, beginners can expand their knowledge by referencing related literature and technical resources, gradually applying these techniques to more complex problems. This code therefore stands as vital learning material for image segmentation and computer vision education, serving as one of the most effective pathways for beginners to understand and master Graph Cuts methodology through hands-on implementation.