Katedra informatiky a výpočetní techniky / Department of Computer Science and Engineering

Kolekce


Recent Submissions

Majdišová, Zuzana , Skala, Václav , Šmolík, Michal
Incremental Meshfree Approximation of Real Geographic Data

In many technical applications, reconstruction of the scattered data is often task. For big scattered dataset in n-dimensional space, the using some meshless method such as the radial basis function (RBF) approximation is appropriate. RBF approximation is based on the distance computation,&#...

Skala, Václav , Šmolík, Michal
Simple and Fast Oexp(N) Algorithm for Finding an Exact Maximum Distance in E2 Instead of O(N^2) or O(N lgN)

Finding a maximum distance of points in E2 or in E3 is one of those. It is a frequent task required in many applications. In spite of the fact that it is an extremely simple task, the known “Brute force” algorithm is of O(N2) complexity. Due to this complex...

Červenka, Martin , Šmolík, Michal , Skala, Václav
A new Strategy for Scattered Data Approximation Using Radial Basis Functions Respecting Points of Inflection

The approximation of scattered data is known technique in computer science. We propose a new strategy for the placement of radial basis functions respecting points of inflection. The placement of radial basis functions has a great impact on the approximation quality. Due to this&#x...

Šmolík, Michal , Skala, Václav
Efficient Simple Large Scattered 3D Vector Fields Radial Basis Functions Approximation Using Space Subdivision

The Radial basis function (RBF) approximation is an efficient method for scattered scalar and vector data fields. However its application is very difficult in the case of large scattered data. This paper presents RBF approximation together with space subdivision technique for large vect...

Šmolík, Michal , Skala, Václav , Majdišová, Zuzana
A new simple, fast and robust total least square error computation in E2: Experimental comparison

Many problems, not only in signal processing, image processing, digital imaging, computer vision and visualization, lead to the Least Square Error (LSE) problem or Total (Orthogonal) Least Square Error (TLSE) problem computation. Usually the standard least square error approximation method is...