Edge Image

Edge image processing focuses on accurately identifying and representing object boundaries in images, crucial for various computer vision tasks. Current research emphasizes improving the quality and precision of edge detection, often employing deep learning models like UNets and convolutional neural networks with innovative feature fusion and loss functions designed to address class imbalances. These advancements are driving progress in applications ranging from medical image analysis (e.g., prostate segmentation) and industrial anomaly detection to improved image inpainting and building footprint extraction from satellite imagery.