We present a method to classify materials in illumination series data. An illumination series is acquired using a device which is capable to generate arbitrary lighting environment...
In this article we propose a method for parameter learning within the energy minimisation framework for segmentation. We do this in an incremental way where user input is required ...
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
Fitting statistical models is a widely employed technique for the segmentation of medical images. While this approach gives impressive results for simple structures, shape models a...
We present a novel variational approach to estimate dense depth maps from multiple images in real-time. By using robust penalizers for both data term and regularizer, our method pr...
Abstract. Approximations based on random Fourier features have recently emerged as an efficient and elegant methodology for designing large-scale kernel machines [4]. By expressing...
Abstract. Image fusion in high-resolution aerial imagery poses a challenging problem due to fine details and complex textures. In particular, color image fusion by using virtual or...
We propose a new approach for integrating geometric scene knowledge into a level-set tracking framework. Our approach is based on a novel constrained-homography transformation mode...
In this paper we present a novel approach for expanding spherical 3D-tensor fields of arbitrary order in terms of a tensor valued local Fourier basis. For an efficient implementati...
Henrik Skibbe, Marco Reisert, Thorsten Schmidt, Kl...