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|a 10.1109/83.806620
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|a eng
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| 100 |
1 |
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|a Bors, A G
|e verfasserin
|4 aut
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| 245 |
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|a Object classification in 3-D images using alpha-trimmed mean radial basis function network
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|c 1999
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 29.06.2010
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| 500 |
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|a Date Revised 12.02.2008
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a We propose a pattern classification based approach for simultaneous three-dimensional (3-D) object modeling and segmentation in image volumes. The 3-D objects are described as a set of overlapping ellipsoids. The segmentation relies on the geometrical model and graylevel statistics. The characteristic parameters of the ellipsoids and of the graylevel statistics are embedded in a radial basis function (RBF) network and they are found by means of unsupervised training. A new robust training algorithm for RBF networks based on alpha-trimmed mean statistics is employed in this study. The extension of the Hough transform algorithm in the 3-D space by employing a spherical coordinate system is used for ellipsoidal center estimation. We study the performance of the proposed algorithm and we present results when segmenting a stack of microscopy images
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| 650 |
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|a Journal Article
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|a Pitas, I
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 8(1999), 12 vom: 28., Seite 1744-56
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|g year:1999
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|u http://dx.doi.org/10.1109/83.806620
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