Nicolas Boutry


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Annoncement of my PhD defense: Affiche-these-NB


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  • Tel: +33 6 76 80 10 53

Short bio

Graduated from ESIEE Paris in 2002 in the field of Signal Processing and Telecommunications, Nicolas Boutry has spent over four years in Switzerland at EPFL where he did research on MR images of human brains and on image compression. He joined then a company called MyCO2 to work on pattern recognition. He realized his Ph.D. thesis on well-composed images from 2014 to 2016 at EPITA on the Olena project and at the LIGM at ESIEE Paris. He is now research assistant at EPITA, still on well-composed images and topological issues in nD discrete images.

My curriculum vitae is available HERE.

Current status

Research Assistant:


  • Algorithmics (TD / TP for 3rd-year students at EPITA)
  • Regular Language Theory (TD / TP for 3rd-year students at EPITA)


Nicolas Boutry, Thierry Geraud, Laurent Najman, How to Make nD Images Well-Composed Without Interpolation, ICIP 2015 PDF (dedicated page: Publications/boutry.15.icip)

Nicolas Boutry, Thierry Geraud, Laurent Najman, How to Make nD Functions Digitally Well-Composed in a Self-Dual Way, ISMM 2015 PDF (dedicated page: Publications/boutry.15.ismm)

Nicolas Boutry, Thierry Geraud, Laurent Najman, Une généralisation du bien-composé à la dimension n, GTGéoDis 2014 PDF, Poster (dedicated page: Publications/boutry.14.geodis)

Nicolas Boutry, Thierry Geraud, Laurent Najman, On Making nD Images Well-Composed by a Self-Dual Local Interpolation, DGCI 2014 PDF (dedicated page: Publications/boutry.14.dgci)

Table of contents of the PhD report: A Study of Well-composedness in n-D


  • AWCness = Alexandrov Well-Composedness
  • DWCness = Digital Well-Composedness
  • CWCness = Continuous Well-Composedness

1 Introduction

2 State-of-the-art
2.1 Mathematical basics
2.2 Well-composed sets and images
2.3 Topological repairing and well-composed interpolations
2.4 Topics related to well-composedness

3 Generalization of well-composedness to dimension n
3.1 The different flavours of n-D WCnesses in brief
3.2 Mathematical basics
3.3 n-D EWCness and n-D DWCness
3.4 Relations between AWCness, DWCness, and CWCness

4 Digitally well-composed Interpolations in n-D
4.1 Self-dual Local Interpolations
4.2 A New Self-dual n-D DWC Interpolation

5 Some consequences and applications
5.1 Pure self-duality
5.2 A new representation of digital images
5.3 n-D Marching-Cubes-like Algorithms
5.4 Tree of shapes of the DWC morphological Laplacian

6 Perspectives
6.1 On the equivalence between AWCness and CWCness on cubical grids
6.2 Preservation of digital well-composedness
6.3 A graph-based characterization of AWCness
6.4 n-D segmentation and parameterization

7 Conclusion

A Proof that min and max interpolations are DWC
B Topological repairing in n-D
C Axiomatic digital topology
D Towards the proof of AWCness and DWCness equivalence
E Well-composed interpolations on polyhedral complexes