It is well known that the interactions between coherent monochromatic radiation and a scattering medium induce a speckle phenomenon. The spatial and temporal statistics of this speckle are employed to analyze many applications in laser imaging. The direct exposure of a photographic film, without a lens to the backscattered radiation, gives a speckle pattern. The main problem lies in the determination of those parameters which can efficiently characterize this pattern. In this paper, we present a fractal-theory-based stochastic approach to approximate the diffusion. In our opinion, this method is more appropriate for the classification of this nonlinear and nonstationary phenomenon than the classical frequency-based approach. The paper also presents several applications of this method which have employed for characterization of different test media.

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In this paper, we investigate both linear and circular stochastic models in the context of texture discrimination. These models aim at representing the magnitudes and orientations obtained by a complex wavelet decomposition, such as the steerable pyramid.The novelty consists in considering specific parametric models for circular data such as von Mises and ψ-distributions to describe the distributions of orientations. Particular attention is paid to the choice of a metric and to its adequation to the models. Indexing experiments are conducted to quantitatively evaluate the performances of the proposed models and of the chosen matrices, i.e. the 1 L and Kullback-Leibler distances.

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It is well known that the interaction between coherent monochromatic radiation and a scattering medium induce a speckle phenomenon. The direct exposure of a photographic film, without a lens to the transmitted radiation, gives speckle pattern. The main problem lies in the determination of parameters which can efficiently characterize this pattern and can be correlated with the optical properties of the medium. In this paper, we present a circular statistics approach to differentiate media.