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Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
This work presents Kornia -- an open source computer vision library whic...
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Mahotas: Open source software for scriptable computer vision
Mahotas is a computer vision library for Python. It contains traditional...
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TorchRadon: Fast Differentiable Routines for Computed Tomography
This work presents TorchRadon – an open source CUDA library which contai...
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BioTracker: An Open-Source Computer Vision Framework for Visual Animal Tracking
The study of animal behavior increasingly relies on (semi-) automatic me...
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Emotion Detection using Image Processing in Python
In this work, user's emotion using its facial expressions will be detect...
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A Multi-Camera Image Processing and Visualization System for Train Safety Assessment
In this paper we present a machine vision system to efficiently monitor,...
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Deluca – A Differentiable Control Library: Environments, Methods, and Benchmarking
We present an open-source library of natively differentiable physics and...
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A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
This work presents Kornia, an open source computer vision library built upon a set of differentiable routines and modules that aims to solve generic computer vision problems. The package uses PyTorch as its main backend, not only for efficiency but also to take advantage of the reverse auto-differentiation engine 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 integrated into neural networks to train models to perform a wide range of operations including 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 on graphical processing units, generating faster systems. Examples of classical vision problems implemented using our framework are provided including a benchmark comparing to existing vision libraries.
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