Blue Gold to Acquire Majority Stake in Ghanaian Gold-Copper Mine

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This paper introduces a novel image segmentation method based on a hybrid approach combining deep learning and graph theory. The proposed technique leverages a convolutional neural network (CNN) for feature extraction followed by a graph-cut algorithm for precise boundary delineation. Experiments on benchmark datasets demonstrate that our method achieves superior segmentation accuracy and outperforms state-of-the-art algorithms, particularly in challenging scenarios with noisy or low-contrast images. The improvement is attributed to the CNN’s robust feature representation and the graph-cut’s ability to enforce global consistency.

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