The paper describes a method that extracts planar roof faces of building objects from point clouds obtained by a pre-segmentation of airborne laserscanner data. The method utilises a TIN-structure that is calculated into the point cloud. The parameters of every TINmesh are mapped into a triangle-mesh parameter space, which is then analysed by cluster analysis techniques. Both the utilisation of...

This paper addresses multidimensional tuning parameter selection in the context of “train-validate-test” and K-fold cross validation. A coarse grid search over tuning parameter space is used to initialize a descent method which then jointly optimizes over variables and tuning parameters. We study four regularized regression methods and develop the update equations for the corresponding descent ...

This paper proposes a new learning method for process neural networks (PNNs) based on the Gaussian mixture functions and particle swarm optimization (PSO), called PSO-LM. First, the weight functions of the PNNs are specified as the generalized Gaussian mixture functions (GGMFs). Second, a PSO algorithm is used to optimize the parameters, such as the order of GGMFs, the number of hidden neurons,...

We derive an efficient reduced basis method for finite volume approximations of parameterized linear advection-diffusion equations. An important step in deriving a reduced finite volume model with the reduced basis technology is the generation of a reduced basis space, on which the detailed numerical simulations are projected. We present a new strategy for this reduced basis generation. We appl...

Traditional fuzzy kernel clustering methods does Iterative clustering in the original data space or in the feature space by mapping the samples into high-dimensional feature space through a kernel function These methods with normalized fuzzy degree of membership has weak robustness against noises and outliers, and lack of effective kernel parameter selection method. To overcome these problems, ...

| In this paper a method for parameterizing orthogonal wavelet transforms is presented. The parameter space is given by the rotation angles of the orthogonal 22{ rotations used in the lattice lters realizing the stages of the wavelet transform. Diierent properties of orthogonal wave-let transforms can be expressed in this parameter space. Then, the parameter space is restricted to the set of ro...

In this paper, a method for parameterizing orthogonal wavelet transforms is presented. The parameter space is given by the rotation angles of the orthogonal 2 2 rotations used in the lattice filters realizing the stages of the wavelet transform. Different properties of orthogonal wavelet transforms can be expressed in this parameter space. Then, the parameter space is restricted to the set of r...

A key problem in visual tracking is how to handle the ambiguity in decision to locate the object effectively using the target appearance model with online update. We address this problem by incorporating sequential clustering and ensemble methods into the tracking system. In this paper, clustering is used for mining the potential historical structure in the parameter space and feature space. Th...

We present a method to determine 3D motion and structure of multiple objects from two perspective views, using adaptive Hough transform. In our method, segmentation is determined based on a 3D rigidity constraint. Instead of searching candidate solutions over the entire five-dimensional translation and rotation parameter space, we only examine the two-dimensional translation space. We divide th...

In this paper we discuss the application of the certified reduced basis method and the associated software package rbMIT c © to “worked problems” in steady and unsteady conduction. Each worked problem is characterized by an input parameter vector — material properties, boundary conditions and sources, and geometry — and desired outputs — selected fluxes and temperatures. The methodology and ass...