Abstract
Self-similar multifractals have a wavelet transform whose maxima define self-similar curves in the scale-space plane. We introduce an algorithm that recovers the affine self-similarity parameters through a voting procedure in the corresponding parameter space. The voting approach is robust with respect to renormalization noises and can recover the value of parameters having random fluctuations. We describe numerical applications to Cantor measures, dyadic multifractals, and the study of diffusion limited aggregates.
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