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
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Feature Registration Framework Using Mixture Models
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
A Robust Algorithm for Point Set Registration Using Mixture of Gaussians
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Point Set Registration: Coherent Point Drift
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rigid and Articulated Point Registration with Expectation Conditional Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
The thin plate spline robust point matching (TPS-RPM) algorithm: A revisit
Pattern Recognition Letters
IEEE Transactions on Image Processing
Smooth point-set registration using neighboring constraints
Pattern Recognition Letters
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In previous works on point registration based on finite mixture model, the correspondence probability is often determined by exploiting global relationship in the point set instead of considering the local point distribution. That results in a simplified registration model. In this paper a feature-dependant finite mixture model (FDMM) is proposed. In particular, an improved descriptor is introduced to describe the local feature of a point. Consequently, a priori density function is formulated for the mixture weights. The unknown parameters of FDMM are computed by maximizing a posteriori (MAP) estimation. Moreover, a bidirectional expectation-maximization (EM) process is introduced to update both point sets in contrast to traditional methods. The performance of our method is demonstrated and validated with carefully designed synthetic data and real data, showing that the proposed method can improve the robustness and accuracy as compared to the traditional registration techniques.