Linear and Non-Linear Model for Statistical Localization of Landmarks

This paper presents and compares 3 methods for the statistical localization of partially occulted landmarks. In many real applications, some information is visible in images and some parts are missing or occulted. These parts are estimated by 3 statistical approaches : a rigid registration, a linear method derived from PCA, which represents spatial relationships, and a non linear model based upon Kernel PCA. Applied to the cephalometric problem, the best method exhibits a mean error of 3.3 mm, which is about 3 times the intra-expert variability.

• Type: Conference
• Year: 2002
• Where: ICPR
• Authors: Barbara Romaniuk, Michel Desvignes, Marinette Revenu, Marie-Josèphe Deshayes
• Keywords: Computer Vision, ICPR 2002, Kernel PCA, Linear Method, Non Linear Model