The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Computational Geometry—A Survey
Abstract
We survey the state of the art of computational geometry, a discipline that deals with the complexity of geometric problems within the framework of the analysis of algorithms. This newly emerged area of activities has found numerous applications in various other disciplines, such as computer-aided design, computer graphics, operations research, pattern recognition, robotics, and statistics. Five major problem areas—convex hulls, intersections, searching, proximity, and combinatorial optimizations—are discussed. Seven algorithmic techniques—incremental construction, plane-sweep, locus, divide-and-conquer, geometric transformation, prune-and-search, and dynamization—are each illustrated with an example. A collection of problem transformations to establish lower bounds for geo-metric problems in the algebraic computation/decision model is also included.
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Citations
Cites
The densest hemisphere problem
Johnson, D. S., & Preparata, F. P. (1978) · Theoretical Computer Science · doi:10.1016/0304-3975(78)90006-3 · Cited by 152
Cited by
Learnability and the Vapnik-Chervonenkis dimension
Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1989) · Journal of the ACM · doi:10.1145/76359.76371 · Cited by 1,839
Generalized delaunay triangulation for planar graphs
Lee, D. T., & Lin, A. K. (1986) · Discrete & Computational Geometry · doi:10.1007/bf02187695 · Cited by 286
Metadata
- Authors
- Lee, & Preparata
- Venue
- IEEE Transactions on Computers
- Published
- 1984-12
- Volume
- C-33
- Issue
- 12
- Pages
- 1072-1101
- ISSN
- 0018-9340 (ISSN-L)
- OpenAlex
- W1981233261
- Cited by
- 283
- References
- 1
- FWCI
- 28.34
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
