D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains, IEEE Signal Processing Magazine, vol.30, issue.3, pp.83-98, 2013.
DOI : 10.1109/MSP.2012.2235192

F. Lozes and A. Elmoataz, Nonlocal Difference Operators on Graphs for Interpolation on Point Clouds, International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, pp.309-316, 2017.
DOI : 10.1109/TIP.2010.2101610

G. Puy, S. Kitic, and P. Pérez, Unifying local and nonlocal signal processing with graph CNNs, 1702.

S. Velasco-forero and J. Angulo, Nonlinear Operators on Graphs via Stacks, Geometric Science of Information , Proceedings, 2015.
DOI : 10.1007/978-3-319-25040-3_70

URL : https://hal.archives-ouvertes.fr/hal-01110976

J. Serra, Image analysis and mathematical morphology, 1982.

H. J. Hejmans, P. Nacken, A. Toet, and L. Vincent, Graph morphology, Journal of Visual Communication and Image Representation, vol.3, issue.1, pp.24-38, 1992.
DOI : 10.1016/1047-3203(92)90028-R

L. Najman and J. Cousty, A graph-based mathematical morphology reader, Pattern Recognition Letters, vol.47, pp.3-17, 2014.
DOI : 10.1016/j.patrec.2014.05.007

URL : https://hal.archives-ouvertes.fr/hal-00986191

P. Salembier, Study on nonlocal morphological operators, 2009 16th IEEE International Conference on Image Processing (ICIP), pp.2269-2272, 2009.
DOI : 10.1109/ICIP.2009.5414374

URL : http://upcommons.upc.edu/bitstream/2117/8832/1/StudyonNonlocal.pdf

S. Velasco-forero and J. Angulo, On Nonlocal Mathematical Morphology, International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, pp.219-230, 2013.
DOI : 10.1007/978-3-642-38294-9_19

URL : https://hal.archives-ouvertes.fr/hal-00834641

G. L. Litvinov, Maslov dequantization, idempotent and tropical mathematics: A brief introduction, Journal of Mathematical Sciences, vol.10, issue.2, pp.426-444, 2007.
DOI : 10.1090/conm/377/07002

URL : http://arxiv.org/pdf/math/0507014

B. A. Carré, An Algebra for Network Routing Problems, IMA Journal of Applied Mathematics, vol.7, issue.3, pp.273-294, 1971.
DOI : 10.1093/imamat/7.3.273

P. Del, M. Moral, and . Doisy, Maslov idempotent probability calculus. II, Theory of Probability & Its Applications, pp.319-332, 2000.

B. Burgeth and J. Weickert, An Explanation for the Logarithmic Connection between Linear and Morphological System Theory, International Journal of Computer Vision, vol.10, issue.3, pp.157-169, 2005.
DOI : 10.1007/3-540-44935-3_54

P. Maragos, Chapter Two -Representations for morphological image operators and analogies with linear operators, of Advances in Imaging and Electron Physics, pp.45-187, 2013.
DOI : 10.1016/b978-0-12-407702-7.00002-4

URL : http://cvsp.cs.ntua.gr/publications/jpubl+bchap/Maragos_OperatorRepresentations_BookChapter_AEIP_2013.pdf

J. Angulo, Chapter One -Convolution in (max,min)algebra and its role in mathematical morphology, of Advances in Imaging and Electron Physics, pp.1-66, 2017.
DOI : 10.1007/978-3-319-18720-4_41

S. Gaubert, Methods and applications of (max,+) linear algebra, 14th Annual Symposium on Theoretical Aspects of Computer Science (STACS), 1997.
DOI : 10.1007/BFb0023465

URL : https://hal.archives-ouvertes.fr/inria-00073603

M. Akian, R. Bapat, and S. Gaubert, Max-plus algebra, Handbook of linear algebra (Discrete Mathematics and its Applications), pp.10-14, 2006.
DOI : 10.1201/b16113-39

S. C. Kleene, Representation of Events in Nerve Nets and Finite Automata, Tech. Rep, 1951.
DOI : 10.1515/9781400882618-002

C. Ronse, Why mathematical morphology needs complete lattices, Signal Processing, vol.21, issue.2, pp.129-154, 1990.
DOI : 10.1016/0165-1684(90)90046-2

G. Peyré, The Numerical Tours of Signal Processing, Computing in Science & Engineering, vol.13, issue.4, pp.94-97, 2011.
DOI : 10.1109/MCSE.2011.71

J. Tournier, F. Calamante, D. G. Gadian, and A. Connelly, Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution, NeuroImage, vol.23, issue.3, pp.1176-1185, 2004.
DOI : 10.1016/j.neuroimage.2004.07.037