A novel method of quantifying the level of detail preservation ability of digital filters is proposed. The method assumes only the input distribution of the filter and estimates how much the filter changes the signal. The change is measured by the expectation of the absolute difference between the input and output signal. The method is applicable for many filters and input distributions. As an example case, the formulas for the expectation of the absolute difference for weighted order statistic filters with the uniform and Laplacian (biexponential) input distributions are derived. Finally, the design of weighted order statistic filters using supervised learning is studied. The learning method uses the detail preservation measure as a design criterion to obtain filters with different levels of detail preservation.
An approach for estimating the distribution of a synchronized budding
yeast (<i>Saccharomyces cerevisiae</i>) cell population is discussed. This involves estimation of the phase of the cell cycle for each cell. The approach is based on counting the number of buds of different sizes in budding yeast images. An image processing procedure is presented for the bud-counting task. The procedure employs clustering of the local mean-variance space for segmentation of the images. The subsequent bud-detection step is based on an object separation method which utilizes the chain code representation of objects as well as labeling of connected components. The procedure is tested with microscopic images that were obtained in a time-series experiment of a synchronized budding yeast cell population. The use of the distribution estimate of the cell population for inverse filtering of signals that are obtained in time-series microarray measurements is discussed as well.
We propose an automatic classification procedure for multichannel remote sensing data. The method consists of several stages. An important stage is the correction of misclassifications based on the use of a nonlinear graph-based estimation technique recently introduced by us. The misclassification correction method is optimized by means of a training-based framework using genetic algorithms. It is shown that this provides a considerable improvement in classification accuracy. After primary local recognition and misclassification correction of all component images, an approach to further use the obtained data is considered. At this joint classification stage we introduce novel subclasses like 'common homogeneous region, common edge, small sized object in one or two components, etc. Numerical simulation data as well as real image processing results are presented to confirm the basic steps of remote sensing data classification and the efficiency of the proposed approach.