Self-image (autoidolon) techniques for the realisation of optical computing type operations

Abstract is not available.

Fracture processes of self compacting Concrete using image analysis

The mode I fracture toughness of concrete can be experimentally determined using three point bend beam in conjunction with digital image correlation (DIC). Three different geometrically similar sizes of beams are cast for this study. To study the influence of fly ash and silica fume on fracture toughness of SCC, three SCC mixes are prepared with and without mineral additions. The scanning electron microscope (SEM) images are taken on the fractured surface to add information on fracture process in SCC. From this study, it is concluded that the fracture toughness of SCC with mineral addition is higher when compared to those without mineral addition.

A comparative study of side-band Fresnel holograms recorded with self-imaging and general objects

This paper deals with new results obtained in regard to the reconstruction properties of side-band Fresnel holograms (SBFH) of self-imaging type objects (for example, gratings) as compared with those of general objects. The major finding is that a distribution I2, which appears on the real-image plane along with the conventional real-image I1, remains a 2Z distribution (where 2Z is the axial distance between the object and its self-imaging plane) under a variety of situations, while its nature and focusing properties differ from one situation to another. It is demonstrated that the two distributions I1 and I2 can be used in the development of a novel technique for image subtraction.

The dynamics of solvation of an electron in the image potential state by a layer of polar adsorbates

Recently, ultrafast two-photon photoemission has been used to study electron solvation at a two-dimensional metal/polar adsorbate interfaces [A. Miller , Science 297, 1163 (2002)]. The electron is bound to the surface by the image interaction. Earlier we have suggested a theoretical description of the states of the electron interacting with a two-dimensional layer of the polar adsorbate [K. L. Sebastian , J. Chem. Phys. 119, 10350 (2003)]. In this paper we have analyzed the dynamics of electron solvation, assuming a trial wave function for the electron and the solvent polarization and then using the Dirac-Frenkel variational method to determine it. The electron is initially photoexcited to a delocalized state, which has a finite but large size, and causes the polar molecules to reorient. This reorientation acts back on the electron and causes its wave function to shrink, which will cause further reorientation of the polar molecules, and the process continues until the electron gets self-trapped. For reasonable values for the parameters, we are able to obtain fair agreement with the experimental observations. (c) 2005 American Institute of Physics.

Design of a FIR filter for image restoration using principal component neural networks

The neural network finds its application in many image denoising applications because of its inherent characteristics such as nonlinear mapping and self-adaptiveness. The design of filters largely depends on the a-priori knowledge about the type of noise. Due to this, standard filters are application and image specific. Widely used filtering algorithms reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design a finite impulse response filter based on principal component neural network (PCNN) is proposed in this study for image filtering, optimized in the sense of visual inspection and error metric. This algorithm exploits the inter-pixel correlation by iteratively updating the filter coefficients using PCNN. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions. Further, the number of unknown parameters is very few and most of these parameters are adaptively obtained from the processed image.

Linearization and Reconstruction of Non-linear Diffuse Optical Tomographic Image

Diffuse optical tomography (DOT) is one of the ways to probe highly scattering media such as tissue using low-energy near infra-red light (NIR) to reconstruct a map of the optical property distribution. The interaction of the photons in biological tissue is a non-linear process and the phton transport through the tissue is modelled using diffusion theory. The inversion problem is often solved through iterative methods based on nonlinear optimization for the minimization of a data-model misfit function. The solution of the non-linear problem can be improved by modeling and optimizing the cost functional. The cost functional is f(x) = x(T)Ax - b(T)x + c and after minimization, the cost functional reduces to Ax = b. The spatial distribution of optical parameter can be obtained by solving the above equation iteratively for x. As the problem is non-linear, ill-posed and ill-conditioned, there will be an error or correction term for x at each iteration. A linearization strategy is proposed for the solution of the nonlinear ill-posed inverse problem by linear combination of system matrix and error in solution. By propagating the error (e) information (obtained from previous iteration) to the minimization function f(x), we can rewrite the minimization function as f(x; e) = (x + e)(T) A(x + e) - b(T)(x + e) + c. The revised cost functional is f(x; e) = f(x) + e(T)Ae. The self guided spatial weighted prior (e(T)Ae) error (e, error in estimating x) information along the principal nodes facilitates a well resolved dominant solution over the region of interest. The local minimization reduces the spreading of inclusion and removes the side lobes, thereby improving the contrast, localization and resolution of reconstructed image which has not been possible with conventional linear and regularization algorithm.