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biblio_chapitre2.bib
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%Le traitement des images (Traité IC2, série Traitement du signal et de l'image)
%MAÎTRE Henri
@Book{maitre,
author = "H. Maitre",
publisher = "Traité IC2, Hermès Paris",
title = "Le traitement des images",
year = 2003,
}
@Book{serra,
author = "J. Serra",
address = "London",
publisher = "Academic Press",
title = "Image Analysis and mathematical Morphology",
year = 1982,
}
@Article{freeman,
author = {H. Freeman},
title = {On Encoding Arbitrary Geometric Configurations},
journal = {IRE Transactions on Electronic Computers},
year = 1961,
volume = 10,
pages = {260--268}
}
@Article{Couprie:2003:DO,
author = "Michel Couprie and Gilles Bertrand and Yukiko
Kenmochi",
title = "Discretization in 2{D} and 3{D} orders",
journal = "Graphical models",
volume = "65",
number = "1--3",
pages = "77--91",
month = may,
year = "2003",
CODEN = "GRMOFM",
ISSN = "1524-0703 (print), 1524-0711 (electronic)",
bibdate = "Wed Nov 5 10:43:01 MST 2003",
acknowledgement = "Nelson H. F. Beebe, University of Utah, Department
of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake
City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1
801 581 4148, e-mail: \path|[email protected]|,
\path|[email protected]|, \path|[email protected]|
(Internet), URL:
\path|http://www.math.utah.edu/~beebe/|",
}
@article{tajine_ronse_h,
author = {Ronse, C. and Tajine, M. },
citeulike-article-id = 869888,
keywords = {digitization},
month = jun,
number = 3,
pages = {219--242},
priority = 2,
title = {Discretization in Hausdorff Space},
volume = 12,
year = 2000,
journal = {Journal of Mathematical Imaging \& Vision},
}
@article{tajine_ronse,
abstract = {We study a new framework for the discretization of closed sets and operators based on Hausdorff metric: a Hausdorff discretization of an n-dimensional Euclidean figure F of , in the discrete space , is a subset S of whose Hausdorff distance to F is minimal ([rho] can be considered as the resolution of the discrete space ); in particular such a discretization depends on the choice of a metric on . This paper is a continuation of our works (Ronse and Tajine, J. Math. Imaging Vision 12 (3) (2000) 219; Hausdorff discretization for cellular distances, and its relation to cover and supercover discretization (to be revised for JVCIR), 2000, Wagner et al., An Approach to Discretization Based on the Hausdorff Metric. I. ISMM'98, Kluwer Academic Publishers, Dordrecht, 1998, pp. 67-74), in which we have studied some properties of Hausdorff discretizations of compact sets.In this paper, we study the properties of Hausdorff discretization for metrics induced by a norm and we refine this study for the class of homogeneous metrics. We prove that for such metrics the popular covering discretizations are Hausdorff discretizations. We also compare the Hausdorff discretization with the Bresenham discretization (Bresenham, IBM Systems J. 4 (1) (1965) 25). Actually, we prove that the Bresenham discretization of a straight line of is not always a good discretization relatively to the Hausdorff metric. This result is an extension of Tajine et al. (Hausdorff Discretization and its Comparison with other Discretization Schemes, DGCI'99, Paris, Lecture Notes in Computer Sciences Vol. 1568, Springer, Berlin, 1999, pp. 399-410), in which we prove the same result for a segment of . Finally, we study how some topological properties of the Euclidean plane are translated in discrete space for Hausdorff discretizations. Actually, we prove that a Hausdorff discretization of a connected closed set is 8-connected and its maximal Hausdorff discretization is 4-connected for homogeneous metrics.},
author = {Tajine, M. and Ronse, C. },
citeulike-article-id = 869887,
doi = {10.1016/S0304-3975(01)00082-2},
journal = {Theoretical Computer Science},
keywords = {digitization},
month = jun,
number = 1,
pages = {243--268},
priority = 2,
title = {Topological properties of Hausdorff discretization, and comparison to other discretization schemes},
url = {http://www.sciencedirect.com/science/article/B6V1G-4606TCJ-C/2/8213e5fa53dd0d8f767a47447526928d},
volume = 283,
year = 2002
}
@Book{filtrage_segmentation,
editor = {P. Bolon and J-M. Chassery and J-P.Cocquerez and D. Demigny and C.Graffigne and
A.Montanvert and S.Philipp and R.Zéboudj and J.Zérubia},
title = {Analyste d'images~: filtrage et segmentation},
publisher = {Masson},
year = 1995
}
@PhdThesis{these_nouvel,
author = {B. Nouvel},
title = {Rotations discrètes et automates cellulaires},
school = {Ecole Normale Supérieure de Lyon},
year = 2006,
month = sep,
}
@article{Andres_standart,
abstract = {A new analytical description model, called the standard model, for the discretization of Euclidean linear objects (point, m-flat, m-simplex) in dimension n is proposed. The objects are defined analytically by inequalities. This allows a global definition independent of the number of discrete points. A method is provided to compute the analytical description for a given linear object. A discrete standard model has many properties in common with the supercover model from which it derives. However, contrary to supercover objects, a standard object does not have bubbles. A standard object is (n-1)-connected, tunnel-free and bubble-free. The standard model is geometrically consistent. The standard model is well suited for modelling applications.},
author = {Andres, Eric },
booktitle = {Special Issue: Discrete Topology and Geometry for Image and Object Representation},
citeulike-article-id = 611322,
doi = {10.1016/S1524-0703(03)00004-3},
journal = {Graphical Models},
keywords = {digitization line model},
month = {May},
number = {1-3},
pages = {92--111},
priority = 0,
title = {Discrete linear objects in dimension n: the standard model},
url = {http://www.sciencedirect.com/science/article/B6WG3-48B5JSB-6/2/bf546f5b3d155ad0645c660af002803e},
volume = 65,
year = 2003
}
@article{kothe_IVC,
abstract = {Computerized image analysis makes statements about the continuous world by looking at a discrete representation. Therefore, it is important to know precisely which information is preserved during digitization. We analyze this question in the context of shape recognition. Existing results in this area are based on very restricted models and thus not applicable to real imaging situations. We present generalizations in several directions: first, we introduce a new shape similarity measure that approximates human perception better. Second, we prove a geometric sampling theorem for arbitrary dimensional spaces. Third, we extend our sampling theorem to two-dimensional images that are subjected to blurring by a disk point spread function. Our findings are steps towards a general sampling theory for shapes that shall ultimately describe the behavior of real optical systems.},
author = {Stelldinger, P. and K{\"o}the, U. },
booktitle = {Discrete Geometry for Computer Imagery},
citeulike-article-id = 761605,
doi = {10.1016/j.imavis.2004.06.003},
journal = {Image and Vision Computing},
keywords = {resolution sampling},
month = feb,
number = 2,
pages = {237--248},
priority = 2,
title = {Towards a general sampling theory for shape preservation},
url = {http://www.sciencedirect.com/science/article/B6V09-4DV1RM0-1/2/7d3d4d34e909fdda36965eece9719266},
volume = 23,
year = 2005
}