Stochastic image pyramids

Abstract A new class of image pyramids is introduced in which a global sampling structure close to that of the twofold reduced resolution next level is generated exclusively by local processes. The probabilistic algorithm exploits local ordering relations among independent identically distributed random variables. The algorithm is superior to any coin tossing based procudure and converges to an optimal sampling structure in only three steps. It can be applied to either 1- or 2-dimensional lattices. Generation of stochastic pyramids has broad applicability. We discuss in detail curve processing in 2-dimensional image pyramids and labeling the mesh in massively parallel computers. We also mention investigation of the robustness of multiresolution algorithms and a fast parallel synthesis method for nonhomogeneous anisotropic random patterns.

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