High Resolution Image Segmentation

High-resolution image segmentation aims to accurately delineate objects and regions within very large images, overcoming computational limitations and preserving fine-grained detail. Current research focuses on improving efficiency through techniques like domain decomposition and adaptive patching, often incorporating transformer architectures and implicit representation mappings to capture both local and global context within the image. These advancements are crucial for applications such as medical imaging, satellite imagery analysis, and autonomous visual inspection, where precise segmentation of high-resolution data is essential for accurate diagnosis, damage assessment, and automated decision-making. The development of robust, computationally efficient methods is driving progress in this field.