Landmarking

Landmarking is the process of identifying and marking a set of predefined reference points, or landmarks, on objects such as images, 3D models, or geographic maps. Landmarks capture geometric structure and local features, enabling quantitative analysis of shape, size, and spatial relationships.

In practice, landmarking can be performed manually by experts or automatically through algorithms. Manual annotation provides high accuracy but is time-consuming. Automatic landmarking relies on computer vision and machine learning, including traditional feature detectors and modern deep learning approaches that regress landmark coordinates or classify patches to locate landmarks. Datasets with labeled landmarks are used for training and validation. Evaluation measures include localization error, mean squared error, and the percentage of landmarks detected within a tolerance threshold.

Applications span many domains. In medicine and biology, landmarks are used for geometric morphometrics, image-guided surgery, and comparative anatomy. In computer vision and graphics, facial landmarking supports face alignment, expression analysis, and 3D reconstruction. In archaeology, landmarks help characterize artifact shapes; in geographic information systems and cartography, landmarks anchor spatial analyses on maps and plans. Landmarking also supports biometric authentication, robotics, and augmented reality.

Challenges include variability in appearance, non-rigid deformation, occlusion, lighting, and limited labeled data. Cross-domain generalization, annotation consistency, and biases can affect performance. Ongoing research aims to improve robustness, reduce annotation burden, and integrate landmarks with statistical shape models and other geometric descriptors to enable more reliable shape analysis.