Stochastic imaging

Thiedmann, R. (2011) New approaches to stochastic image segmentation and modeling of complex microstructures -application to the analysis of advanced materials, PhD thesis, Ulm University. Baddeley, A.J. and Cruz-Orive, LM. (1995) The Rao-Blackwell theorem in stereology and some counterexamples. Adv. Appl. Probab., 27, 2-19. 

The prior knowledge is assumed to be the discrete structure of the image, the statistical independence of the noise values, their stationarity and zero mean value. For this case, the image reconstruction problem can be represented as an adaptive stochastic estimation process [9] with the structure shown in Fig. 1. 

Rust, M. J., Bates, M. and Zhuang, X. (2006). Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793-5. 

While the Kriged image plots are useful for thinking about how the chemical patterns might be distributed across space, we would like to point out that here we assume that the chemical concentrations are spatially dependent and that they vary stochastically across space. We make this assumption based on our knowledge of the micro-soilscapes of the plaza and patio, which are highly... 

Detailed description of a porous microstructure is an essential prerequisite for unveiling the influence of pore morphology on the underlying two-phase behavior. This can be achieved either by 3-D volume imaging or by constructing a digital microstructure based on stochastic reconstruction models. Non-invasive techniques, such as X-ray micro-tomography, are the popular methods for 3-D... 

Balaji, J., and Ryan, T. A. (2007). Single-vesicle imaging reveals that synaptic vesicle exocytosis and endocytosis are coupled by a single stochastic mode. Proc. Natl. Acad. Sci USA 104, 20576—20581. 

When we have discrete stochastic models, as those introduced through the polystochastic chains, we can obtain their image by using different methods the Z transformation, the discrete Fourier transformation, the characteristic function of... 

For example, periodic boundary conditions can be applied [124] (although special consideration must be given to the interaction of the QM group with its images), or where only part of a protein can be included, the stochastic boundary method for dynamics can be applied [9,125]. 

In this section, two examples are presented for the application of a technique of low-melting-point alloy (LMPA) impregnation that provides for a visualization of the invasion of a nonwetting fluid into the pore spaces in a typical porous article. The visualization can be linked to the modeling of mercury porosimeter curves using 3-D stochastic pore networks. This makes the quantification of pore structure more direct. Quantified structures can be visually examined against sample particle sections. The visual comparison can be made more precise by image analysis of the accessible porosity made visible by metal penetration over a series of pressures. 

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