39 citations · 126 across the 13 of their papers we have counts for
13 papers · 1 filter
Exemplar: Few-Shot Biomedical Segmentation That Refits Exactly in Seconds
Michal Průšek, Adam Novozámský, Filip Šroubek
Segmenting a new microscopy dataset usually means training a deep network on its annotations, and training again for every added image. Exemplar solves a segmentation readout in cl…
HyperBank: A Differentiable Bank of Classical Priors for Few-Shot Spheroid Microscopy Segmentation
M. Průšek, A. Novozámský, F. Šroubek +3
Few-shot spheroid segmentation must adapt to new cell lines, microscopes, and illumination conditions from only a small set of annotated images. While foundation few-shot segmenter…
Harmformer: Harmonic Networks Meet Transformers for Continuous Roto-Translation Equivariance
Tomáš Karella, Adam Harmanec, Jan Kotera +2
CNNs exhibit inherent equivariance to image translation, leading to efficient parameter and data usage, faster learning, and improved robustness. The concept of translation equivar…
H-NeXt: The next step towards roto-translation invariant networks
Tomas Karella, Filip Sroubek, Jan Flusser +2
The widespread popularity of equivariant networks underscores the significance of parameter efficient models and effective use of training data. At a time when robustness to unseen…
NeRD: Neural field-based Demosaicking
Tomas Kerepecky, Filip Sroubek, Adam Novozamsky +1
We introduce NeRD, a new demosaicking method for generating full-color images from Bayer patterns. Our approach leverages advancements in neural fields to perform demosaicking by r…
Real-Time Wheel Detection and Rim Classification in Automotive Production
Roman Stanek, Tomas Kerepecky, Adam Novozamsky +3
This paper proposes a novel approach to real-time automatic rim detection, classification, and inspection by combining traditional computer vision and deep learning techniques. At…
Blur Invariants for Image Recognition
Jan Flusser, Matej Lebl, Matteo Pedone +2
Blur is an image degradation that is difficult to remove. Invariants with respect to blur offer an alternative way of a~description and recognition of blurred images without any de…
FMODetect: Robust Detection of Fast Moving Objects
Denys Rozumnyi, Jiri Matas, Filip Sroubek +2
We propose the first learning-based approach for fast moving objects detection. Such objects are highly blurred and move over large distances within one video frame. Fast moving ob…
Learning-based Tracking of Fast Moving Objects
Ales Zita, Filip Sroubek
Tracking fast moving objects, which appear as blurred streaks in video sequences, is a difficult task for standard trackers as the object position does not overlap in consecutive v…
Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objects
Denys Rozumnyi, Jan Kotera, Filip Sroubek +1
We propose a novel method that tracks fast moving objects, mainly non-uniform spherical, in full 6 degrees of freedom, estimating simultaneously their 3D motion trajectory, 3D pose…
Non-Causal Tracking by Deblatting
Denys Rozumnyi, Jan Kotera, Filip Šroubek +1
Tracking by Deblatting stands for solving an inverse problem of deblurring and image matting for tracking motion-blurred objects. We propose non-causal Tracking by Deblatting which…
Intra-frame Object Tracking by Deblatting
Jan Kotera, Denys Rozumnyi, Filip Šroubek +1
Objects moving at high speed along complex trajectories often appear in videos, especially videos of sports. Such objects elapse non-negligible distance during exposure time of a s…
The World of Fast Moving Objects
Denys Rozumnyi, Jan Kotera, Filip Sroubek +2
The notion of a Fast Moving Object (FMO), i.e. an object that moves over a distance exceeding its size within the exposure time, is introduced. FMOs may, and typically do, rotate w…