This paper presents a comprehensive overview of graph cuts and their application to efficient N-D image segmentation. The authors introduce a novel approach to object/background segmentation based on s-t graph cuts, which has shown great potential for solving various problems in vision and graphics. The method combines boundary regularization with region-based properties in the same way as Mumford-Shah style functionals. The paper discusses the theoretical foundations of graph cuts, their relationship to previous segmentation methods, and their practical applications in computer vision and graphics. It also covers the technical details of the basic combinatorial optimization framework for image segmentation via s/t graph cuts, including the formulation of segmentation energy, the integration of regional and boundary cues, and the use of hard constraints to restrict the search space of feasible solutions. The paper also discusses the efficiency and robustness of graph cut methods, their ability to handle N-D problems, and their connection to other segmentation techniques such as snakes, geodesic active contours, and level-sets. The authors also provide a detailed description of the graph cut framework, including the construction of the graph, the definition of edge weights, and the computation of the minimum cost cut. The paper concludes with a discussion of the practical implications of the graph cut approach, including its ability to handle real-world applications such as medical imaging and video processing.This paper presents a comprehensive overview of graph cuts and their application to efficient N-D image segmentation. The authors introduce a novel approach to object/background segmentation based on s-t graph cuts, which has shown great potential for solving various problems in vision and graphics. The method combines boundary regularization with region-based properties in the same way as Mumford-Shah style functionals. The paper discusses the theoretical foundations of graph cuts, their relationship to previous segmentation methods, and their practical applications in computer vision and graphics. It also covers the technical details of the basic combinatorial optimization framework for image segmentation via s/t graph cuts, including the formulation of segmentation energy, the integration of regional and boundary cues, and the use of hard constraints to restrict the search space of feasible solutions. The paper also discusses the efficiency and robustness of graph cut methods, their ability to handle N-D problems, and their connection to other segmentation techniques such as snakes, geodesic active contours, and level-sets. The authors also provide a detailed description of the graph cut framework, including the construction of the graph, the definition of edge weights, and the computation of the minimum cost cut. The paper concludes with a discussion of the practical implications of the graph cut approach, including its ability to handle real-world applications such as medical imaging and video processing.