L
Luc Van Gool
Researcher at Katholieke Universiteit Leuven
Publications - 1458
Citations - 137230
Luc Van Gool is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 133, co-authored 1307 publications receiving 107743 citations. Previous affiliations of Luc Van Gool include Microsoft & ETH Zurich.
Papers
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Book ChapterDOI
The CAMETRON Lecture Recording System: High Quality Video Recording and Editing with Minimal Human Supervision
Dries Hulens,Bram Aerts,Punarjay Chakravarty,Ali Diba,Toon Goedemé,Tom Roussel,Jeroen Zegers,Tinne Tuytelaars,Luc Van Eycken,Luc Van Gool,Hugo Van hamme,Joost Vennekens +11 more
TL;DR: This paper demonstrates a system that automates the process of recording video lectures in classrooms through special hardware (lecturer and audience facing cameras and microphone arrays), and an automatic video editing system is used to naturally switch between the different video streams.
Book ChapterDOI
Euclidean 3D Reconstruction from Stereo Sequences with Variable Focal Lengths
TL;DR: In this paper, a stereo rig can be calibrated using a calibration grid, but recent work demonstrated the possibility of auto-calibration, however, there remain two important limitations, however. First, the focal lengths of the cameras should remain fixed, thereby excluding zooming or focusing.
Book ChapterDOI
Grouping Based on Coupled Diffusion Maps
Marc Proesmans,Luc Van Gool +1 more
TL;DR: Systems of coupled, non-linear diffusion equations are proposed as a computational tool for grouping and it is shown how different cues can be used for grouping given these two blueprints plus cue-specific specialisations.
3D modeling and registration under wide baseline conditions
Luc Van Gool,Tinne Tuytelaars,Vittorio Ferrari,Christoph Strecha,Joris Vanden Wyngaerd,Maarten Vergauwen +5 more
Posted Content
Self-Supervised Shadow Removal.
TL;DR: This work proposes an unsupervised single image shadow removal solution via self-supervised learning by using a conditioned mask, which largely improves quantitatively and qualitatively over the compared methods and set a new state-of-the-art performance in single imageshadow removal.