Registration with Uncertainties and Statistical Modeling of Shapes with Variable Metric Kernels
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[1] Alain Trouvé,et al. Diffeomorphisms Groups and Pattern Matching in Image Analysis , 1998, International Journal of Computer Vision.
[2] Bernhard Schölkopf,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.
[3] K. S. Arun,et al. Least-Squares Fitting of Two 3-D Point Sets , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Anil K. Jain,et al. Model-guided segmentation of corpus callosum in MR images , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).
[5] Eric J. Pauwels,et al. Cluster-based segmentation of natural scenes , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[6] Gunilla Borgefors,et al. Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..
[7] Gerald Farin,et al. Deformation with hierarchical B-splines , 2001 .
[8] Johan Montagnat,et al. A hybrid framework for surface registration and deformable models , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[9] C. R. Deboor,et al. A practical guide to splines , 1978 .
[10] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .
[11] Charles V. Stewart,et al. Simultaneous Covariance Driven Correspondence (CDC) and Transformation Estimation in the Expectation Maximization Framework , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[12] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[13] C. Goodall. Procrustes methods in the statistical analysis of shape , 1991 .
[14] Fred L. Bookstein,et al. Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..
[15] D. Comaniciu,et al. The variable bandwidth mean shift and data-driven scale selection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[16] Nikos Paragios,et al. Establishing Local Correspondences towards Compact Representations of Anatomical Structures , 2003, MICCAI.
[17] Matthew P. Wand,et al. Kernel Smoothing , 1995 .
[18] Eero P. Simoncelli. Bayesian multi-scale differential optical flow , 1999 .
[19] Mineichi Kudo,et al. MDL-Based Selection of the Number of Components in Mixture Models for Pattern Classification , 1998, SSPR/SPR.
[20] Thomas W. Sederberg,et al. Free-form deformation of solid geometric models , 1986, SIGGRAPH.
[21] Karl Rohr,et al. Approximating Thin-Plate Splines for Elastic Registration: Integration of Landmark Errors and Orientation Attributes , 1999, IPMI.
[22] Daniel Rueckert,et al. Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.
[23] Nikos Paragios,et al. Modelling shapes with uncertainties: higher order polynomials, variable bandwidth kernels and non parametric density estimation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[24] A. ROSENFELD,et al. Distance functions on digital pictures , 1968, Pattern Recognit..
[25] Olivier D. Faugeras,et al. Statistical shape influence in geodesic active contours , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).
[26] Nikos Paragios,et al. Shape registration in implicit spaces using information theory and free form deformations , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] O. Faugeras. Three-dimensional computer vision: a geometric viewpoint , 1993 .
[28] Nikos Paragios,et al. Non-rigid registration using distance functions , 2003, Comput. Vis. Image Underst..
[29] Gérard G. Medioni,et al. Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.
[30] S. Sain. Multivariate locally adaptive density estimation , 2002 .
[31] Paul J. Besl,et al. A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..
[32] Karl Rohr,et al. Spline-based elastic image registration: integration of landmark errors and orientation attributes , 2003, Comput. Vis. Image Underst..
[33] Benjamin B. Kimia,et al. The Shock Scaffold for Representing 3D Shape , 2001, IWVF.
[34] Dorin Comaniciu,et al. Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[35] Daniel Cremers,et al. Shape statistics in kernel space for variational image segmentation , 2003, Pattern Recognit..
[36] C. Stewart. Uncertainty-Driven, Point-Based Image Registration , 2006, Handbook of Mathematical Models in Computer Vision.
[37] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[38] Xavier Pennec,et al. A Framework for Uncertainty and Validation of 3-D Registration Methods Based on Points and Frames , 2004, International Journal of Computer Vision.
[39] Kenichi Kanatani,et al. Uncertainty modeling and model selection for geometric inference , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[40] Laurent D. Cohen,et al. Image Registration, Optical Flow and Local Rigidity , 2001, Journal of Mathematical Imaging and Vision.
[41] Zhengyou Zhang,et al. Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.
[42] Nikos Paragios,et al. Motion-based background subtraction using adaptive kernel density estimation , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[43] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[44] Carl de Boor,et al. A Practical Guide to Splines , 1978, Applied Mathematical Sciences.
[45] D. Mumford,et al. Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .
[46] Timothy F. Cootes,et al. A mixture model for representing shape variation , 1999, Image Vis. Comput..
[47] Anthony Widjaja,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.
[48] Nikos Paragios,et al. Uncertainty-Driven Non-parametric Knowledge-Based Segmentation: The Corpus Callosum Case , 2005, VLSM.
[49] Haifeng Chen,et al. Robust Computer Vision through Kernel Density Estimation , 2002, ECCV.
[50] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[51] Olivier D. Faugeras,et al. Approximations of Shape Metrics and Application to Shape Warping and Empirical Shape Statistics , 2005, Found. Comput. Math..
[52] Alan L. Yuille,et al. Deformable Templates for Feature Extraction from Medical Images , 1990, ECCV.
[53] Timothy F. Cootes,et al. Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..
[54] Ahmed M. Elgammal,et al. High Resolution Acquisition, Learning and Transfer of Dynamic 3‐D Facial Expressions , 2004, Comput. Graph. Forum.
[55] James S. Duncan,et al. Boundary Finding with Parametrically Deformable Models , 1992, IEEE Trans. Pattern Anal. Mach. Intell..
[56] Ralph Roskies,et al. Fourier Descriptors for Plane Closed Curves , 1972, IEEE Transactions on Computers.
[57] Chia-Ling Tsai,et al. The dual-bootstrap iterative closest point algorithm with application to retinal image registration , 2003, IEEE Transactions on Medical Imaging.
[58] Gérard G. Medioni,et al. Object modelling by registration of multiple range images , 1992, Image Vis. Comput..
[59] Dorin Comaniciu,et al. The Variable Bandwidth Mean Shift and Data-Driven Scale Selection , 2001, ICCV.
[60] Alejandro F. Frangi,et al. Independent component analysis in statistical shape models , 2003, SPIE Medical Imaging.
[61] Nikos Paragios,et al. Motion-based background subtraction using adaptive kernel density estimation , 2004, CVPR 2004.