Biomedical subjects

Luc Van Gool

Publications and source records attributed to Luc Van Gool.

2 recordsLinked to original sources

PubMed2006

Real-time range acquisition by adaptive structured light.

The goal of this paper is to provide a "self-adaptive" system for real-time range acquisition. Reconstructions are based on a single frame structured light illumination. Instead of using generic, static coding that is supposed to work under all circumstances, system adaptation is proposed. This occurs on-the-fly and renders the system more robust against instant scene variability and creates suitable patterns at startup. A continuous trade-off between speed and quality is made. A weighted combination of different coding cues--based upon pattern color, geometry, and tracking--yields a robust way to solve the correspondence problem. The individual coding cues are automatically adapted within a considered family of patterns. The weights to combine them are based on the average consistency with the result within a small time-window. The integration itself is done by reformulating the problem as a graph cut. Also, the camera-projector configuration is taken into account for generating the projection patterns. The correctness of the range maps is not guaranteed, but an estimation of the uncertainty is provided for each part of the reconstruction. Our prototype is implemented using unmodified consumer hardware only and, therefore, is cheap. Frame rates vary between 10 and 25 fps, dependent on scene complexity.

Algorithms↗

PubMed2004

Drift detection and removal for sequential structure from motion algorithms.

In sequential Structure from Motion algorithms for extended image or video sequences, error build up caused by drift poses a problem as feature tracks that normally represent a single scene point will have distinct 3D reconstructions. For the final bundle adjustment to remove this drift, it must be told about these 3D-3D correspondences through a change in the cost function. However, as a bundle adjustment is a nonlinear optimization technique, the drift needs to be removed from the supplied initial solution to allow for convergence of the bundle adjustment to the real global optimum. Before drift can be removed, it has to be detected. This is accomplished through understanding of the long term behavior of drift which leaves 3D reconstructions from short sequences intact. Drift detection boils down to identifying reconstructions of the same scene part that only differ up to a projective transformation. After detection, the drift can be removed from future processed images and an Adapted Bundle Adjustment using correspondences supplied by the drift detection can remove the drift from previous images. Several experiments on real video sequences demonstrate the merit of drift detection and removal.

Algorithms↗