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Most cost function based clustering or partitioning methods measure the compactness of groups of data. In contrast to this picture of a point source in feature space, some data sources are spread out on a low-dimensional manifold which is embedded in a high dimensional data space. This property is adequately captured by the criterion of connectedness which(More)
Immunohistochemical and in situ hybridization techniques were used to investigate the neuroanatomical distribution of arginine vasotocin-like systems in the roughskin newt (Taricha granulosa). Vasotocin-like-immunoreactive neuronal cell bodies were identified that, based on topographical position, most likely, are homologous to groups of(More)
Thyroid hormone is essential for normal brain development. Therefore, it is a genuine concern that thyroid function can be altered by a very large number of chemicals routinely found in the environment and in samples of human and wildlife tissues. These chemicals range from natural to manufactured compounds. They can produce thyroid dysfunction when they(More)
Unsupervised Image Segmentation is one of the central issues in Computer Vision. From the viewpoint of exploratory data analysis, segmentation can be formulated as a clustering problem in which pix-els or small image patches are grouped together based on local feature information. In this contribution, parametrical distributional clustering (PDC) is(More)
Automated segmentation of images has been considered an important intermediate processing task to extract semantic meaning from pixels. We propose an integrated approach for image segmentation based on a generative clustering model combined with coarse shape information and robust parameter estimation. The sensitivity of segmentation solutions to image(More)
In this contribution we analyze communication requirements of multi-agent simulation systems using IT-SimBw -- developed at Fraunhofer IAIS -- as an example. A focus is put on issues concerning inter-agent communication but complementary aspects of user interaction and coupling with C2 systems are also discussed. We propose an augmented version of the(More)
Unsupervised image segmentation can be formulated as a clustering problem in which pixels or small image patches are grouped together based on local feature information. In this contribution, parametric distributional clustering (PDC) is presented as a novel approach to image segmen-tation based on color and texture clues. The objective function of the PDC(More)
DISCLAIMER The use of company or product name(s) is for identification only and does not imply endorsement by the Agency for Toxic Substances and Disease Registry. A Toxicological Profile for Phenol, Draft for Public Comment was released in October 2006. This edition supersedes any previously released draft or final profile. Toxicological profiles are(More)
The automated segmentation of images into semantically meaningful parts requires shape information since lowlevel feature analysis alone often fails to reach this goal. We introduce a novel method of shape constrained image segmentation which is based on mixtures of feature distributions for color and texture as well as probabilistic shape knowledge. The(More)