Curvature-based representation of objects from range data
Image and Vision Computing
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least squares conformal maps for automatic texture atlas generation
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
ACM Transactions on Graphics (TOG)
A New Paradigm for Recognizing 3-D Object Shapes from Range Data
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Efficient Shape Matching Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-rigid surface registration using spherical thin-plate splines
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
BoostMap: a method for efficient approximate similarity rankings
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
2D-shape analysis using conformal mapping
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A landmark-based brain conformal parametrization with automatic landmark tracking technique
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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In this paper, we present a novel and efficient surface matching framework through shape image representation. This representation allows us to simplify a 3D surface matching problem to a 2D shape image matching problem. Furthermore, we present a shape image diffusion-based method to find the most robust features to construct the matching and registration of surfaces. This is particularly important for inter-subject surfaces from medical scans of different subjects since these surfaces exhibit the inherited physiological variances among subjects. We conducted extensive experiments on real 3D human neocortical surfaces, which demonstrate the excellent performance of our approach in terms of accuracy and robustness.