3 citations · 3 across the 4 of their papers we have counts for
10 papers · 1 filter
Deep vanishing point detection: Geometric priors make dataset variations vanish
Yancong Lin, Ruben Wiersma, Silvia L. Pintea +3
Deep learning has improved vanishing point detection in images. Yet, deep networks require expensive annotated datasets trained on costly hardware and do not generalize to even sli…
Semi-supervised lane detection with Deep Hough Transform
Yancong Lin, Silvia-Laura Pintea, Jan van Gemert
Current work on lane detection relies on large manually annotated datasets. We reduce the dependency on annotations by leveraging massive cheaply available unlabelled data. We prop…
No frame left behind: Full Video Action Recognition
Xin Liu, Silvia L. Pintea, Fatemeh Karimi Nejadasl +2
Not all video frames are equally informative for recognizing an action. It is computationally infeasible to train deep networks on all video frames when actions develop over hundre…
Deep Hough-Transform Line Priors
Yancong Lin, Silvia L. Pintea, Jan C. van Gemert
Classical work on line segment detection is knowledge-based; it uses carefully designed geometric priors using either image gradients, pixel groupings, or Hough transform variants.…
Top-Down Networks: A coarse-to-fine reimagination of CNNs
Ioannis Lelekas, Nergis Tomen, Silvia L. Pintea +1
Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and prefe…
Using phase instead of optical flow for action recognition
Omar Hommos, Silvia L. Pintea, Pascal S. M. Mettes +1
Currently, the most common motion representation for action recognition is optical flow. Optical flow is based on particle tracking which adheres to a Lagrangian perspective on dyn…