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We address three-dimensional (3D) visualization and recognition of microorganisms using single-exposure on-line (SEOL) digital holography. A coherent 3D microscope-based Mach-Zehnder interferometer records a single on-line Fresnel digital hologram of microorganisms. Three-dimensional microscopic images are reconstructed numerically at different depths by an inverse Fresnel transformation. For recognition, microbiological objects are segmented by processing the background diffraction field. Gabor-based wavelets extract feature vectors with multi-oriented and multi-scaled Gabor kernels. We apply a rigid graph matching (RGM) algorithm to localize predefined shape features of biological samples. Preliminary experimental and simulation results using sphacelaria alga and tribonema aequale alga microorganisms are presented. To the best of our knowledge, this is the first report on 3D visualization and recognition of microorganisms using on-line digital holography with single-exposure.

This paper concerns a class of network location problems with minimum or maximum separation requirements between (uncapacitated) facilities or between demand points and the facilities, or both. Its purpose is three-fold. First, it is to recognize distance constraints as increasing real life restrictions through various motivating illustrations. Using a new classification scheme, the paper introduces a variety of distance-constrained problems defined in a unified manner. These include a number of new problems. Second, it is to survey existing solution techniques, available only for a few of such constrained problems. Finally, it is to shed some light on yet unstudied problems by exploring possible extensions of some of the known solution techniques or discussing varying degrees of difficulties involved. In particular, the paper presents integer programming formulations of several new problems along with the results of applying linear programming relaxation methods. Although the computational experience is somewhat disappointing for some of these problems, the results provide greater insight into the problems. With the stated purpose, it is hoped that this paper will stimulate future research in this important problem area.facilities/equipment planning: location

as a preprocessing step, image segmentation, which can do partition of an image into different regions, plays an important role in computer vision, objects recognition, tracking and image analysis. Till today, there are a large number of methods present that can extract the required foreground from the background. However, most of these methods are solely based on boundary or regional information which has limited the segmentation result to a large extent. Since the graph cut based segmentation method was proposed, it has obtained a lot of attention because this method utilizes both boundary and regional information. Furthermore, graph cut based method is efficient and accepted world-wide since it can achieve globally optimal result for the energy function. It is not only promising to specific image with known information but also effective to the natural image without any pre-known information. For the segmentation of N-dimensional image, graph cut based methods are also applicable. Due to the advantages of graph cut, various methods have been proposed. In this paper, the main aim is to help researcher to easily understand the graph cut based segmentation approach. We also classify this method into three categories. They are speed up-based graph cut, interactive-based graph cut and shape prior-based graph cut. This paper will be helpful to those who want to apply graph cut method into their research.

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