This paper provides an overview of Chamfer Matching, a classical image registration method primarily used for segmented features. They explain its theoretical underpinnings, including its reliance on distance transforms, cost functions, and optimization algorithms. The texts highlight its applications, particularly in medical imaging for radiotherapy, where it aids in treatment verification, planning, and quantifying organ motion. Additionally, the sources touch upon its use in computer vision for object detection, such as pedestrian tracking, while acknowledging its strengths and limitations regarding factors like accuracy, speed, and robustness to image quality and outliers.
