Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Šroubek, Filip"'
CNNs exhibit inherent equivariance to image translation, leading to efficient parameter and data usage, faster learning, and improved robustness. The concept of translation equivariant networks has been successfully extended to rotation transformatio
Externí odkaz:
http://arxiv.org/abs/2411.03794
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 deformations is becoming increasingly important, we present H-NeXt, w
Externí odkaz:
http://arxiv.org/abs/2311.01111
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 representing an image as a coordinate-based neural network with sine ac
Externí odkaz:
http://arxiv.org/abs/2304.06566
Autor:
Stanek, Roman, Kerepecky, Tomas, Novozamsky, Adam, Sroubek, Filip, Zitova, Barbara, Flusser, Jan
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 the end of every automotive assembly line, a quality control process i
Externí odkaz:
http://arxiv.org/abs/2304.06560
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 deblurring. In this paper, we present an original unified theory of blur
Externí odkaz:
http://arxiv.org/abs/2301.07581
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
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 objects are associated with a deblurring and matting problem, also calle
Externí odkaz:
http://arxiv.org/abs/2012.08216
Autor:
Zita, Ales, Sroubek, Filip
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 video frames and texture information of the objects is blurred. Up-to-d
Externí odkaz:
http://arxiv.org/abs/2005.01802
Publikováno v:
2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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 and object appearance changes with a time step that is a fraction of
Externí odkaz:
http://arxiv.org/abs/1911.10927