Voronoi tessellation of points with integer coordinates: Time-efficient implementation and online edge-list generation
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bio1About the Author—ROBERT L. OGNIEWICZ received the M.S. and the Ph.D. in electrical engineering from the Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, in 1988 and 1992, respectively. From 1992 to 1994 he was a postdoctoral fellow at the Communication Technology Laboratory of the ETH. Currently, he is a postdoctoral fellow at the Division of Applied Sciences, Harvard University, Cambridge, U.S.A. His research interests include pattern recognition, computer vision and
References (20)
- et al.
Computational Geometry
Algorithms in Combinatorial Geometry
(1987)- et al.
Voronoi skeletons: theory and applications
Discrete Voronoi Skeletons
(1993)- et al.
Continuous skeleton computation by Voronoi diagram
Comput. Vision, Graphics Image Process
(1992) - et al.
Hierarchic Voronoi skeletons
Pattern Recognition
(1995) Exact Euclidean distance function by chain propagation
The Euclidean Distance Transform
Dot pattern processing using Voronoi neighborhoods
IEEE Trans. Pattern Recognition Mach. Intell.
(1982)- et al.
Extraction of early perceptual structure in dot patterns: integrating region, boundary, and component Gestalt
Comput. Vision, Graphics Image Process
(1989)
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2005, NeurocomputingCitation Excerpt :The advantage of encoding space using this transform is that two-dimensional space is encoded into vectors, sym-ax pieces, of one spatial dimension, which is more convenient for object representation than using the entire region. Numerous algorithms have been developed that evolve the SAT using different techniques (see [25] for a review) and have been applied to problems like general shape encoding [21,27], letter recognition [15], medical image analysis [8] and motion coding [17], to mention only a few examples. Many of these algorithms are concerned with an exact reconstruction of the shape and often work on shapes that possess or even require closed and complete contours.
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bio1About the Author—ROBERT L. OGNIEWICZ received the M.S. and the Ph.D. in electrical engineering from the Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, in 1988 and 1992, respectively. From 1992 to 1994 he was a postdoctoral fellow at the Communication Technology Laboratory of the ETH. Currently, he is a postdoctoral fellow at the Division of Applied Sciences, Harvard University, Cambridge, U.S.A. His research interests include pattern recognition, computer vision and artificial intelligence, with special interest in shape representation and recognition. He is a member of the IEEE and the IEEE Computer Society.
bio2About the Author—OLAF KÜBLER received an education in theoretical physics at the Universities of Karlsruhe and Heidelberg, Germany, and the Swiss Federal Institute of Technology (ETH) in Zurich, Switzerland. He obtained the doctorate in theoretical nuclear physics in 1970. In 1972, he joined the Institute of Cell Biology at ETH-Zurich to direct research in processing electron microscopical images of molecular structures. Since 1979, he is Professor of Image Science at the Electrical Engineering Department of ETH-Zurich. Research activities at his laboratory involve segmentation of 2D and 3D image data, percetual grouping, description and representation of 2D and 3D shapes, and systems for quantitative image evaluation. Main application areas are analysis of 3D medical MRI data, Robot Vision and interpretation of aerial photographs.