Statistics of rough surfaces via remote sensing An experiment

Abstract . The process for fabricating a random rough surface with given statistics is described . The technique consists of producing a random rough surface on plates covered with photoresist by exposing the plates to speckle patterns . The theory presented by Alvarez-Borrego is compared with experimental results obtained from the fabricated surface using a mechanical profilometer . The aim is to compare the statistical properties obtained with the Alvarez-Borrego with the results obtained using other methods . The results obtained with the mechanical profilometer agree reasonably well with those obtained from the model . . Introduction Recently, a new method for obtaining some statistical properties of surface height (when the lateral scale of the height fluctuations is greater than the wavelength of the light) from the statistical properties of the intensities in the image, via remote sensing, was presented by Alvarez-Borrego [1] . Attention is focused on the statistics of surface height to estimate its standard deviation or variance . The motivation for this work is the application of this nonlinear technique for obtaining statistical information from a real sea surface using aerial photographs . The wave data can be readily and accurately collected by aerial photographs of the wave sun glint patterns which show reflections of the sun and sky light from the water and thus offer high-contrast wave images . In the present work we describe the method for surface fabrication and the experimental set-up used to obtain the glitter pattern images . A random bi-dimensional rough surface with known Gaussian statistics was made in the laboratory to compare the statistical properties obtained from the model with the results obtained from other methods . Finally, a discussion of the results obtained with the model and with other methods, such as the profilometric technique, are presented . Surface fabrication The technique used for the fabrication of our bi-dimensional surface is a variation of a technique described originally by Gra

Fast autofocus algorithm for automated microscopes.

We present a new algorithm to determine, quickly and accurately, the best-in-focus image of biological particles. The algorithm is based on a one-dimensional Fourier transform and on the Pearson correlation for automated microscopes along the Z axis. We captured a set of several images at different Z distances from a biological sample. The algorithm uses the Fourier transform to obtain and extract the image frequency content of a vector pattern previously specified to be sought in each captured image; comparing these frequency vectors with the frequency vector of a reference image (usually the first image that we capture or the most out-of-focus image), we find the best-in-focus image via the Pearson correlation. Numerical experimental results show the algorithm has a fast response for finding the best-in-focus image among the captured images, compared with related autofocus techniques presented in the past. The algorithm can be implemented in real-time systems with fast response, accuracy, and robustness; it can be used to get focused images in bright and dark fields; and it offers the prospect of being extended to include fusion techniques to construct multifocus final images