Commit 5d635919 authored by Celian GOSSEC's avatar Celian GOSSEC

Update with skeleton for LT 2020 #1

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@InProceedings{ levillain.14.ciarp,
author = {Roland Levillain and Thierry G\'eraud and Laurent Najman
and Edwin Carlinet},
title = {Practical Genericity: Writing Image Processing Algorithms
Both Reusable and Efficient},
booktitle = {Progress in Pattern Recognition, Image Analysis, Computer
Vision, and Applications -- Proceedings of the 19th
Iberoamerican Congress on Pattern Recognition (CIARP)},
address = {Puerto Vallarta, Mexico},
month = nov,
year = {2014},
pages = {70--79},
editor = {Eduardo Bayro and Edwin Hancock},
publisher = {Springer-Verlag},
series = {Lecture Notes in Computer Science},
volume = {8827},
lrdeprojects = {Olena},
abstract = {An important topic for the image processing and pattern
recognition community is the construction of open source
and efficient libraries. An increasing number of software
frameworks are said to be generic: they allow users to
write reusable algorithms compatible with many input image
types. However, this design choice is often made at the
expense of performance. We present an approach to preserve
efficiency in a generic image processing framework, by
leveraging data types features. Variants of generic
algorithms taking advantage of image types properties can
be defined, offering an adjustable trade-off between
genericity and efficiency. Our experiments show that these
generic optimizations can match dedicated code in terms of
execution times, and even sometimes perform better than
routines optimized by hand. Digital Topology software
should reflect the generality of the underlying
mathematics: mapping the latter to the former requires
genericity. By designing generic solutions, one can
effectively reuse digital topology data structures and
algorithms. We propose an image processing framework
focused on the Generic Programming paradigm in which an
algorithm on the paper can be turned into a single code,
written once and usable with various input types. This
approach enables users to design and implement new methods
at a lower cost, try cross-domain experiments and help
generalize results.},
keywords = {Generic Programming, Image Processing, Performance,
lrdepaper = {},
lrdeslides = {},
lrdenewsdate = {2014-09-10}
@InProceedings{ roynard.18.rrpr,
title = {An Image Processing Library in Modern {C++}: Getting
Simplicity and Efficiency with Generic Programming},
author = {Micha\"el Roynard and Edwin Carlinet and Thierry G\'eraud},
booktitle = {Proceedings of the 2nd Workshop on Reproducible Research
in Pattern Recognition (RRPR)},
year = {2018},
abstract = {As there are as many clients as many usages of an Image
Processing library, each one may expect different services
from it. Some clients may look for efficient and
production-quality algorithms, some may look for a large
tool set, while others may look for extensibility and
genericity to inter-operate with their own code base... but
in most cases, they want a simple-to-use and stable
product. For a C++ Image Processing library designer, it is
difficult to conciliate genericity, efficiency and
simplicity at the same time. Modern C++ (post 2011) brings
new features for library developers that will help
designing a software solution combining those three points.
In this paper, we develop a method using these facilities
to abstract the library components and augment the
genericity of the algorithms. Furthermore, this method is
not specific to image processing; it can be applied to any
C++ scientific library.}
author = {Wenzel Jakob and Jason Rhinelander and Dean Moldovan},
year = {2017},
note = {},
title = {pybind11 -- Seamless operability between C++11 and Python}
author={Niebler, Eric},
\date[27-02-2020]{Lightning Talk \#1: Feb 27, 2020}
\author{Celian \textsc{Gossec}}
\title[Parallelism in Pylene]{Introducing parallelism in a generic image
processing framework for fun \& performances}
\institute[LRDE]{LRDE\\\textit{Laboratoire de Recherche et Développement de l'EPITA}}
\frametitle{Rappel: la situation}
\structure{Point de départ.} Milena, une bibliothèque \emph{générique et performante}
de traitement d'image codée en C++.
\footnote{\tiny Practical Genericity : Writing Image Processing Algorithms Both Reusable
and Efficient.R. Levillain et al., \textit{ICPR'14}.}
\footnote{\tiny An Image Processing Library in Modern C++: Getting Simplicity and Efficiency
with Generic Programming. M. Roynard, E. Carlinet, T. Géraud, \textit{RRPR'18}.}\\[1pt]
\structure{Objectif.} Faciliter l'usage d'Olena à travers une interface
\item Appeler du code \textit{statique} (templates) depuis un
environnement \textit{dynamique}\\
\item Compatibilité avec Numpy
\frametitle{La progression}
\item Implémentation d'un container, \textit{any\_ref}, inspiré de Boost
\footnote{\tiny Boost. E. Niebler et al., \textit{1999}.}
\item Implémentation d'une méthode de conversion d'un type à un autre dans les \textit{value\_set}
On obtient ainsi des résultats très concrets, comme le montre la présentation qui suit!
\frametitle{Les prochains pas}
\item Rajouter les différentes fonctions non implémentées qui servent à traiter des images 2D
\item Permettre l'utilisation de coercision en tandem avec de la répartition dynamique
\item Utiliser la compilation à la volée quand/si possible
\nocite{levillain.14.ciarp, roynard.18.rrpr, pybind11, niebler1999boost}
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