Stefan Pszczólkowski

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In this paper, we propose an image registration algorithm named statistically-based FFD registration (SFFD). This registration method is a modification of a well-known free-form deformations (FFD) approach. Our framework dramatically reduces the number of parameters to optimise and only needs to perform a single-resolution optimisation to account for coarse(More)
In order to deploy mobile robots in social environments like indoor buildings, they need to be provided with perceptual abilities to detect people. In the computer vision literature the most typical solution to this problem is based on background subtraction techniques, however, in the case of a mobile robot this is not a viable solution. This paper shows(More)
Nonrigid image registration is a widely used technique in medical imaging. While most methods work very well on images without pathologies or artefacts, there is a high need for improved robustness on images from pathological subjects and acquisitions with artefacts such as intensity inhomogeneity. In this paper, we propose a novel similarity measure based(More)
This paper describes the main activities and achievements of our research group on Machine Intelligence and Robotics (Grima) at the Computer Science Department , Pontificia Universidad Catolica de Chile (PUC). Since 2002, we have been developing an active research in the area of indoor autonomous social robots. Our main focus has been the cognitive side of(More)
A potentially large anatomical variability among subjects in a population makes nonrigid image registration techniques prone to inaccuracies and to high computational costs in their optimisation. In this paper, we propose a new learning-based approach to accelerate the convergence rate of any chosen parametric energy-based image registration method. From a(More)
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