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Image‐to‐Geometry Registration: a Mutual Information Method exploiting Illumination‐related Geometric Properties
Author(s) -
Corsini Massimiliano,
Dellepiane Matteo,
Ponchio Federico,
Scopigno Roberto
Publication year - 2009
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2009.01552.x
Subject(s) - mutual information , image registration , computer science , computer vision , similarity measure , artificial intelligence , similarity (geometry) , measure (data warehouse) , information geometry , transformation geometry , range (aeronautics) , domain (mathematical analysis) , image (mathematics) , mathematics , geometry , data mining , mathematical analysis , materials science , scalar curvature , curvature , composite material
This work concerns a novel study in the field of image‐to‐geometry registration. Our approach takes inspiration from medical imaging, in particular from multi‐modal image registration. Most of the algorithms developed in this domain, where the images to register come from different sensors (CT, X‐ray, PET), are based on Mutual Information , a statistical measure of non‐linear correlation between two data sources. The main idea is to use mutual information as a similarity measure between the image to be registered and renderings of the model geometry, in order to drive the registration in an iterative optimization framework. We demonstrate that some illumination‐related geometric properties, such as surface normals, ambient occlusion and reflection directions can be used for this purpose. After a comprehensive analysis of such properties we propose a way to combine these sources of information in order to improve the performance of our automatic registration algorithm. The proposed approach can robustly cover a wide range of real cases and can be easily extended.