Kernel Methods for Implicit Surface Modeling

TL;DR: In this article, a mean-shift based clustering procedure is proposed for robust filtering of a noisy set of points sampled from a smooth surface using a kernel density estimation technique for point clustering.

TL;DR: A direct definition of the function from the points as the solution to a constrained quadratic optimization problem, which uses a single parameter (parameter-free for exact interpolation), applies to any dimensions, commutes with similarity transformations, and can be easily implemented without discretizing the space.

TL;DR: This work proposes statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields between two objects based on Support Vector Machines, and applies the method to the morphing of 3D heads and other objects.

TL;DR: In this paper, the authors proposed a statistical learning method for approximating implicit surfaces and computing dense 3D deformation fields between two objects. And they applied the method to the morphing of 3D heads and other objects.

TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?

TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.

TL;DR: In this paper, the authors propose a method to estimate a function f that is positive on S and negative on the complement of S. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space.

TL;DR: The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data by carrying out sequential optimization over pairs of input patterns and providing a theoretical analysis of the statistical performance of the algorithm.