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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Proceedings ArticleDOI
NTIRE 2018 Challenge on Image Dehazing: Methods and Results
TL;DR: This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results and gauges the state-of-the-art in imageDehazing.
Interactive museum guide : fast and robust recognition of museum objects
TL;DR: It is demonstrated that both the object recognition performance as well as the speed of the SURF algorithm surpasses the results obtained with SIFT, its main contender.
Book ChapterDOI
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
TL;DR: In this article, a Curriculum Model Adaptation (CMAda) method is proposed to gradually adapt a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both synthetic and real foggy data.
Journal Article
Object Detection by Contour Segment Networks
TL;DR: In this article, the image edges are partitioned into contour segments and organized in an image representation which encodes their interconnections, and the object detection problem is formulated as finding paths through the network resembling the model outlines, and a computationally efficient detection technique is presented.
Proceedings ArticleDOI
Crossing Nets: Combining GANs and VAEs with a Shared Latent Space for Hand Pose Estimation
TL;DR: In this article, the authors propose a semi-supervised generative model for 3D hand pose estimation from depth images, where the generator is updated with the back-propagated gradient from the discriminator to synthesize realistic depth maps.