3 citations · 12 across the 19 of their papers we have counts for
24 papers
Deep kernel video approximation for unsupervised action segmentation
Silvia L. Pintea, Jouke Dijkstra
This work focuses on per-video unsupervised action segmentation, which is of interest to applications where storing large datasets is either not possible, or nor permitted. We prop…
Skip priors and add graph-based anatomical information, for point-based Couinaud segmentation
Xiaotong Zhang, Alexander Broersen, Gonnie CM van Erp +2
The preoperative planning of liver surgery relies on Couinaud segmentation from computed tomography (CT) images, to reduce the risk of bleeding and guide the resection procedure. U…
Top-K Maximum Intensity Projection Priors for 3D Liver Vessel Segmentation
Xiaotong Zhang, Alexander Broersen, Gonnie CM van Erp +2
Liver-vessel segmentation is an essential task in the pre-operative planning of liver resection. State-of-the-art 2D or 3D convolution-based methods focusing on liver vessel segmen…
Continuous and complete liver vessel segmentation with graph-attention guided diffusion
Xiaotong Zhang, Alexander Broersen, Gonnie CM van Erp +2
Improving connectivity and completeness are the most challenging aspects of liver vessel segmentation, especially for small vessels. These challenges require both learning the cont…
A step towards understanding why classification helps regression
Silvia L. Pintea, Yancong Lin, Jouke Dijkstra +1
A number of computer vision deep regression approaches report improved results when adding a classification loss to the regression loss. Here, we explore why this is useful in prac…
Is there progress in activity progress prediction?
Frans de Boer, Jan C. van Gemert, Jouke Dijkstra +1
Activity progress prediction aims to estimate what percentage of an activity has been completed. Currently this is done with machine learning approaches, trained and evaluated on c…
Objects do not disappear: Video object detection by single-frame object location anticipation
Xin Liu, Fatemeh Karimi Nejadasl, Jan C. van Gemert +2
Objects in videos are typically characterized by continuous smooth motion. We exploit continuous smooth motion in three ways. 1) Improved accuracy by using object motion as an addi…
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…
NeRD++: Improved 3D-mirror symmetry learning from a single image
Yancong Lin, Silvia-Laura Pintea, Jan van Gemert
Many objects are naturally symmetric, and this symmetry can be exploited to infer unseen 3D properties from a single 2D image. Recently, NeRD is proposed for accurate 3D mirror pla…
Equal Bits: Enforcing Equally Distributed Binary Network Weights
Yunqiang Li, Silvia L. Pintea, Jan C. van Gemert
Binary networks are extremely efficient as they use only two symbols to define the network: . One can make the prior distribution of these symbols a design choice. The r…
Frequency learning for structured CNN filters with Gaussian fractional derivatives
Nikhil Saldanha, Silvia L. Pintea, Jan C. van Gemert +1
Frequency information lies at the base of discriminating between textures, and therefore between different objects. Classical CNN architectures limit the frequency learning through…
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…
Hand-tremor frequency estimation in videos
Silvia L. Pintea, Jian Zheng, Xilin Li +3
We focus on the problem of estimating human hand-tremor frequency from input RGB video data. Estimating tremors from video is important for non-invasive monitoring, analyzing and d…
Recurrent knowledge distillation
Silvia L. Pintea, Yue Liu, Jan C. van Gemert
Knowledge distillation compacts deep networks by letting a small student network learn from a large teacher network. The accuracy of knowledge distillation recently benefited from…
Deja Vu: Motion Prediction in Static Images
Silvia L. Pintea, Jan C. van Gemert, Arnold W. M. Smeulders
This paper proposes motion prediction in single still images by learning it from a set of videos. The building assumption is that similar motion is characterized by similar appeara…
Featureless: Bypassing feature extraction in action categorization
Silvia L. Pintea, Pascal S. Mettes, Jan C. van Gemert +1
This method introduces an efficient manner of learning action categories without the need of feature estimation. The approach starts from low-level values, in a similar style to th…
Asymmetric kernel in Gaussian Processes for learning target variance
Silvia L. Pintea, Jan C. van Gemert, Arnold W. M. Smeulders
This work incorporates the multi-modality of the data distribution into a Gaussian Process regression model. We approach the problem from a discriminative perspective by learning,…
Video Acceleration Magnification
Yichao Zhang, Silvia L. Pintea, Jan C. van Gemert
The ability to amplify or reduce subtle image changes over time is useful in contexts such as video editing, medical video analysis, product quality control and sports. In these co…
One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network
Vedran Vukotić, Silvia-Laura Pintea, Christian Raymond +2
There is an inherent need for autonomous cars, drones, and other robots to have a notion of how their environment behaves and to anticipate changes in the near future. In this work…
Making a Case for Learning Motion Representations with Phase
S. L. Pintea, J. C. van Gemert
This work advocates Eulerian motion representation learning over the current standard Lagrangian optical flow model. Eulerian motion is well captured by using phase, as obtained by…