Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Multiresolution analysis of arbitrary meshes
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
A Stochastic Iterative Closest Point Algorithm (stochastICP)
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Automated Texture Registration and Stitching for Real World Models
PG '00 Proceedings of the 8th Pacific Conference on Computer Graphics and Applications
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Alignment by Maximization of Mutual Information
Alignment by Maximization of Mutual Information
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Fast and Simple Stretch-Minimizing Mesh Parameterization
SMI '04 Proceedings of the Shape Modeling International 2004
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Genetic Algorithm SAmple Consensus (GASAC) - A Parallel Strategy for Robust Parameter Estimation
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Stochastic optimization of multiple texture registration using mutual information
Proceedings of the 29th DAGM conference on Pattern recognition
Fully Automatic Registration of Image Sets on Approximate Geometry
International Journal of Computer Vision
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Intensity based registration methods, such as the mutual information (MI), do not commonly consider the spatial geometric information and the initial correspondences are uncertainty In this paper, we present a novel approach for achieving highly-automatic 2D/3D image registration integrating the advantages from both entropy MI and spatial geometric features correspondence methods Inspired by the scale space theory, we project the surfaces on a 3D model to 2D normal image spaces provided that it can extract both local geodesic feature descriptors and global spatial information for estimating initial correspondences for image-to-image and image-to-model registration The multiple 2D/3D image registration can then be further refined using MI The maximization of MI is effectively achieved using global stochastic optimization To verify the feasibility, we have registered various artistic 3D models with different structures and textures The high-quality results show that the proposed approach is highly-automatic and reliable.