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By Petra Perner, Ovidio Salvetti

This publication constitutes the refereed lawsuits of the overseas convention on Mass facts research of signs and pictures in medication, Biotechnology and Chemistry, MDA 2006 and 2007, held in Leipzig, Germany..

The themes comprise options and advancements of sign and photograph generating methods, item matching and item monitoring in microscopic and video microscopic photos, 1D, 2nd and 3D form research, description and have extraction of texture, constitution and placement, photograph segmentation algorithms, parallelization of snapshot research and semantic tagging of pictures from existence technology applications.

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Perner and O. ): MDA 2006/2007, LNAI 4826, pp. 27–36, 2007. © Springer-Verlag Berlin Heidelberg 2007 28 G. Ramella and G. Sanniti di Baja Histogram thresholding is computationally convenient, but does not take into account spatial information. Thus, it may be difficult to identify a single threshold for image binarization. This happens, for instance, when the same gray-levels characterize pixels that a user would classify, depending on the local context, in some parts of the image as belonging to the background, and in other parts of the image as belonging to the foreground.

This confirms the result using the complex Bingham distribution and the |λ2 | values. 3). Table 1. Computed absolute values of λ2 and result of the uniformity test for the real datasets. The last row lists the number of BAC-triangles for each dataset (sum: 457). 26 68 44 S. Yang et al. Fig. 3. The results of generalized Procrustes method and the corresponding percentage of variability captured by the i-th PC: Datasets AR1 (a), AR2 (b), stable triangles (c), and random triangles (d) In Fig. 3 the results of the generalized Procrustes method are shown.

Pixels belonging to each connected component of 3’s, 2s and 1’s could be labeled with the average color of the corresponding region in the input image. We have noted that, if this method is adopted, some region merging becomes necessary. We are currently working on this problem. 4 Conclusion In this paper we have presented a segmentation scheme for gray-level images based on two processes: non-topological erosion and topological expansion. The method is semi-automatic since the user is requested to select, based on the gray-levels histogram, two thresholds, θi and θf, to assign to the background and to the foreground pixels definitely perceived as belonging to these two sets.

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