@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,
                  Olena},
  lrdepaper     = {http://www.lrde.epita.fr/dload/papers/levillain.14.ciarp.pdf},
  lrdeslides    = {http://www.lrde.epita.fr/dload/papers/levillain.14.ciarp.slides.pdf},
  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.}
}

@inproceedings{expsimd,
author = {Malossi, A. Cristiano I. and Ineichen, Yves and Bekas, Costas and Curioni, Alessandro},
year = {2015},
month = {01},
pages = {},
title = {Fast Exponential Computation on SIMD Architectures},
doi = {10.13140/2.1.4362.3207}
}

@article{tiling_performances,
author = {Wittenbrink, Craig and Somani, Arun},
year = {1993},
month = {01},
pages = {12-22},
title = {Cache tiling for high performance morphological image processing},
volume = {7},
journal = {Machine Vision and Applications},
doi = {10.1007/BF01212412}
}