Journal article
Multidimensional alignment using the Euclidean distance transform
Graphical models and image processing, v 59(6), pp 373-387
01 Nov 1997
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We present a methodology for alignment of multidimensional data sets that is based on the Euclidean distance transform and the Marquardt-Levenberg optimization algorithm. The proposed approach operates on pixel or voxel descriptions of objects to be matched and estimates the parameters of a space transformation for optimal alignment of objects. The computational cost of an algorithm developed with this method is estimated. The methodology is tested by developing an algorithm for rigid body transformation alignment of three-dimensional data sets. Tests with synthetic and real objects indicate that the method is accurate, reliable, and robust.
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Details
- Title
- Multidimensional alignment using the Euclidean distance transform
- Creators
- Dorota Kozinska - Nencki Institute of Experimental Biology,Warsaw,PolandOleh Tretiak - Drexel UniversityJonathan Nissanov - Drexel UniversityCengizhan Ozturk - Drexel University
- Publication Details
- Graphical models and image processing, v 59(6), pp 373-387
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- [Retired Faculty]
- Web of Science ID
- WOS:000074147300001
- Scopus ID
- 2-s2.0-0031274550
- Other Identifier
- 991019167858604721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Software Engineering