Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
An extension of the Munkres algorithm for the assignment problem to rectangular matrices
Communications of the ACM
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Robust Point Matching for Nonrigid Shapes by Preserving Local Neighborhood Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphical Models and Point Pattern Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Shape matching and registration by data-driven EM
Computer Vision and Image Understanding
The mixtures of Student's t-distributions as a robust framework for rigid registration
Image and Vision Computing
Point Set Registration: Coherent Point Drift
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Point Set Registration Using Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This work addresses the problem of non-rigid registration between two 2D or 3D points sets as a novel application of Relevance Vector Machines (RVM). An iterative framework is proposed which consists of two steps: at first, correspondences between distinct points are estimated by the Hungarian algorithm and then a regression procedure based on a Bayesian linear model (RVM) maps the two sets of points. By these means, a large variety of transformation is captured without imposing any prior knowledge on the form of the point sets. The proposed algorithm provides a smooth transformation even if the correspondence between the points in the two sets contains erroneous matches. The algorithm was successfully evaluated on sets of points with varying difficulty and favorably compared with state-of-the-art methods in cases of noise.