Guilherme Holsbach Costa

arXiv2026

On the Performance and Implementation of Parallax free Video See-Through Displays

In see-through systems an observer watches a (background) scene partially occluded by a display. In this display, usually positioned close to the observer, a region of the background scene is shown, yielding the sensation that the display is transparent. To achieve the transparency effect, it is very important to compensate the parallax error and other distortions caused by the image acquisition system. In this paper a detailed study of a video see-through methodology with parallax correction is performed. In a system composed by two cameras -- one directed to the user and another to the background scene -- and a display, the relative position between the user, the display and the scene is estimated using a feature detection algorithm and the parallax error is compensated assuming a planar scene model. The application of the proposed methodology on Driver Assistance Systems (DAS) is proposed. A theoretical assessment of the algorithm shows that although approximations are proposed to simplify the methodology and reduce the computational cost, such as the planar scene model and fixed working distance, on some practical situations their effects can be neglected without noticeable impact on the perceptual quality of the solution.

cs.GR↗

arXiv2018

A New Adaptive Video Super-Resolution Algorithm With Improved Robustness to Innovations

In this paper, a new video super-resolution reconstruction (SRR) method with improved robustness to outliers is proposed. Although the R-LMS is one of the SRR algorithms with the best reconstruction quality for its computational cost, and is naturally robust to registration inaccuracies, its performance is known to degrade severely in the presence of innovation outliers. By studying the proximal point cost function representation of the R-LMS iterative equation, a better understanding of its performance under different situations is attained. Using statistical properties of typical innovation outliers, a new cost function is then proposed and two new algorithms are derived, which present improved robustness to outliers while maintaining computational costs comparable to that of R-LMS. Monte Carlo simulation results illustrate that the proposed method outperforms the traditional and regularized versions of LMS, and is competitive with state-of-the-art SRR methods at a much smaller computational cost.

cs.CV↗