Semantic Image Segmentation

Semantic image segmentation aims to classify each pixel in an image into predefined categories, enabling detailed scene understanding. Current research focuses on improving accuracy and robustness through advanced architectures like U-Net and Transformers, incorporating contextual information from depth maps, video sequences, and hyperspectral data, and addressing challenges like domain adaptation and handling noisy or distorted inputs. This field is crucial for numerous applications, including autonomous driving, medical image analysis, and remote sensing, where precise pixel-level labeling is essential for effective decision-making.