A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Scattered Data Interpolation with Multilevel B-Splines
IEEE Transactions on Visualization and Computer Graphics
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-rigid registration using distance functions
Computer Vision and Image Understanding - Special issue on nonrigid image registration
A 2D Range Hausdorff Approach for 3D Face Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Shape Registration in Implicit Spaces Using Information Theory and Free Form Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper introduces a new global-to-local shape registration technique and shows its potential in solving the problem of 3D face recognition in the presence of expressions. The proposed registration technique is a two-step technique that operates in an implicit higher dimensional space where the powerful distance transform is used as the embedding function. First, a new dissimilarity measure is introduced to recover the transformation that globally aligns the two input shapes. This new measure can deal efficiently with rigid, similarity and affine motions. Second, the local coordinate transformation between the two globally aligned shapes is explicitly estimated by minimizing a new energy functional consisting of three terms. The first term is a discrepancy measure between the two shape representations. The second term penalizes the deviation of the distance map representation of the globally warped source shape from a signed distance function, while the local displacement field is being updated. The last term is a regularization term that enforces the smoothness of the recovered deformations. This leads to a set of coupled equations that are simultaneously minimized through a gradient descent scheme. The overall potential of the proposed framework is demonstrated through various 2D/3D experimental results. As an application, we address the 3D face recognition problem in presence of facial expressions.