Geometric Optimization Problems in Computer Vision & Medical Imaging: Multimodal 3D Volume Registration, Discrete & Differential Pose Estimation von Park, Frank Chongwoo; Lee, Seok: New (2008)

Beschreibung

Bestandsnummer des Verk�ufers I-9783639032260

Bibliografische Details

Titel: Geometric Optimization Problems in Computer Vision & Medical Imaging: Multimodal 3D Volume Registration, Discrete & Differential Pose Estimation
Verlag: VDM Verlag
Ver�ffentlichungsdatum: 2008
Sprache: Englisch
ISBN-10: 3639032268
ISBN-13: 9783639032260
Bindung: Softcover
Zustand: New

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Inhaltsangabe

We explore various optimization problems which appear in computer vision and image processing. By exploiting the geometric structure of the underlying search spaces, we develop more efficient and robust ways to find optimal solutions. We present the Nelder-Mead algorithms on Lie groups and describe its application to medical image registration. A widely used approach to image registration involves finding the transformation that maximizes the mutual information between two images, with the transformation restricted to be either rigid-body (i.e., belonging to SE(3)) or volume-preserving (i.e., belonging to SL(3)). We present coordinate-invariant, geometric versions of the Nelder-Mead optimization algorithm on the transformation groups SL(3), SE(3), and its various subgroups, that are applicable to a wide class of image registration problems. Because the algorithms respect the geometric structure of the underlying transformation groups, they are numerically more stable, and exhibit better convergence properties than existing local coordinate-based algorithms. Experimental results demonstrate the improved convergence properties of our geometrics algorithm.

Rese�a del editor

We explore various optimization problems which appear in computer vision and image processing. By exploiting the geometric structure of the underlying search spaces, we develop more efficient and robust ways to find optimal solutions. We present the Nelder-Mead algorithms on Lie groups and describe its application to medical image registration. A widely used approach to image registration involves finding the transformation that maximizes the mutual information between two images, with the transformation restricted to be either rigid-body (i.e., belonging to SE(3)) or volume-preserving (i.e., belonging to SL(3)). We present coordinate-invariant, geometric versions of the Nelder-Mead optimization algorithm on the transformation groups SL(3), SE(3), and its various subgroups, that are applicable to a wide class of image registration problems. Because the algorithms respect the geometric structure of the underlying transformation groups, they are numerically more stable, and exhibit better convergence properties than existing local coordinate-based algorithms. Experimental results demonstrate the improved convergence properties of our geometrics algorithm.

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