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with 0 < C(t) < + For the issue of the MBM simulation, see [AYA 00], where there are some indications regarding this question We will also note the existence of the FracLab toolbox in this eld 643 Towards more irregularities: generalized multifractional Brownian motion (GMBM) and step fractional Brownian motion (SFBM) We saw in the preceding section that the MBM provides a model for locally self-similar rocesses with varying multifractional functions and pointwise exponents However, Hypothesis 64 essential within the strict framework of MBM is cumbersome for certain applications Let us quote two examples where we would wish for regularities which are worse than H lderian regularities First, in the rupture models which are important in image segmentation, we wish that the H lder exponent had some discontinuities Let us be clearer about this problem through a metaphor Let us suppose that we have an aerial image on which we want to distinguish the limit of a eld and a forest It is usual to model the texture of a forest by a FBM of the H lder exponent H1 In the same way, for the portion of the eld, we can think of a FBM of exponent H2 The question arises as to how to connect the two processes on the border There is a possibility of considering a MBM within the meaning of De nition 67, for which function h takes the two values H1 and H2 However, this MBM does not correspond to the image Indeed, it shows a discontinuity at the place where function h jumps However, on an image, only the regularity changes suddenly and most often, the eld remains continuous To model this type of rupture, let us recall the construction of the step fractional Brownian motion (SFBM) of [BEN 00] In addition, the H lder exponent of the MBM varies very slowly for applications with so-called developed turbulence (see [FRI 97] for an introduction to this subject) Indeed, the physics of turbulence teach us that the accessible data from measurements are not the H lder exponents of the studied quantities, but their multifractal spectrum.

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