Deep learning is used for image segmentation because it can achieve high accuracy by learning complex spatial patterns and pixel-level relationships. Convolutional neural networks (CNNs) automatically extract hierarchical features, making them ideal for segmenting objects with varying shapes, textures, and sizes. Advanced models like U-Net and Mask R-CNN enable precise delineation of object boundaries, even in complex scenes. Deep learning also benefits from large datasets and GPUs, allowing models to generalize well across diverse conditions, which is critical for applications like medical imaging and autonomous vehicles.
Why do we use deep learning for image segmentation?
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