Graph Cuts: An Image Segmentation Method
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Graph Cuts is a highly popular image segmentation method that employs graph theory-based algorithms to partition images. One of its key characteristics is enabling interactive segmentation, allowing users to adjust segmentation boundaries according to their requirements while achieving superior segmentation quality. The core principle of Graph Cuts involves defining an energy function to quantify segmentation quality, then applying min-cut/max-flow algorithms to determine the optimal segmentation solution. This approach is widely implemented in image processing through functions like graph construction from pixel neighborhoods and energy minimization using max-flow solvers. Beyond image segmentation, the method extends to data segmentation and analysis across various domains. Overall, Graph Cuts combines intuitive implementation with high flexibility and accuracy, making it extensively adopted and recognized in practical applications.
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