Skeletonization Algorithm

Skeletonization algorithms aim to create simplified, skeletal representations of shapes from various data sources, such as images or point clouds, preserving key topological and geometric features. Current research focuses on improving efficiency and accuracy through machine learning approaches, particularly convolutional neural networks (CNNs) and graph-based methods, often incorporating attention mechanisms for improved long-range dependencies in complex shapes. These advancements are impacting diverse fields, including medical image analysis (e.g., blood vessel segmentation, anatomical modeling), robotics (e.g., robotic pruning), and computer vision (e.g., shape analysis, object recognition), by providing efficient and accurate shape representations for various tasks.