Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00341237" target="_blank" >RIV/68407700:21230/20:00341237 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/WACV45572.2020.9093363" target="_blank" >https://doi.org/10.1109/WACV45572.2020.9093363</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/WACV45572.2020.9093363" target="_blank" >10.1109/WACV45572.2020.9093363</a>
Alternative languages
Result language
angličtina
Original language name
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
Original language description
This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be inserted inside neural networks to train models to perform image transformations, camera calibration, epipolar geometry, and low level image processing techniques such as filtering and edge detection that operate directly on high dimensional tensor representations. Examples of classical vision problems implemented using our framework are also provided including a benchmark comparing to existing vision libraries.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
ISBN
978-1-7281-6553-0
ISSN
2472-6737
e-ISSN
2642-9381
Number of pages
10
Pages from-to
3663-3672
Publisher name
IEEE
Place of publication
New Jersey
Event location
Snowmass village
Event date
Mar 1, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000578444803078