Computational Geometry: Algorithms and ApplicationsSpringer Science & Business Media, 2000 - 367 Seiten This well-accepted introduction to computational geometry is a textbook for high-level undergraduate and low-level graduate courses. The focus is on algorithms and hence the book is well suited for students in computer science and engineering. Motivation is provided from the application areas: all solutions and techniques from computational geometry are related to particular applications in robotics, graphics, CAD/CAM, and geographic information systems. For students this motivation will be especially welcome. Modern insights in computational geometry are used to provide solutions that are both efficient and easy to understand and implement. All the basic techniques and topics from computational geometry, as well as several more advanced topics, are covered. The book is largely self-contained and can be used for self-study by anyone with a basic background in algorithms. In the second edition, besides revisions to the first edition, a number of new exercises have been added. |
Inhalt
Computational Geometry Introduction | 1 |
Convex Hulls | 2 |
12 Degeneracies and Robustness | 8 |
13 Application Domains | 10 |
14 Notes and Comments | 13 |
15 Exercises | 15 |
Line Segment Intersection Thematic Map Overlay | 19 |
21 Line Segment Intersection | 20 |
91 Triangulations of Planar Point Sets | 185 |
92 The Delaunay Triangulation | 188 |
93 Computing the Delaunay Triangulation | 191 |
94 The Analysis | 197 |
95 A Framework for Randomized Algorithms | 200 |
96 Notes and Comments | 206 |
97 Exercises | 207 |
More Geometric Data Structures Windowing | 211 |
22 The DoublyConnected Edge List | 29 |
23 Computing the Overlay of Two Subdivisions | 33 |
24 Boolean Operations | 39 |
25 Notes and Comments | 40 |
26 Exercises | 42 |
Polygon Triangulation Guarding an Art Gallery | 45 |
31 Guarding and Triangulations | 46 |
32 Partitioning a Polygon into Monotone Pieces | 49 |
33 Triangulating a Monotone Polygon | 55 |
34 Notes and Comments | 59 |
35 Exercises | 60 |
Linear Programming Manufacturing with Molds | 63 |
41 The Geometry of Casting | 64 |
42 HalfPlane Intersection | 66 |
43 Incremental Linear Programming | 71 |
44 Randomized Linear Programming | 77 |
45 Unbounded Linear Programs | 80 |
46 Linear Programming in Higher Dimensions | 82 |
47 Smallest Enclosing Discs | 86 |
48 Notes and Comments | 90 |
49 Exercises | 91 |
Orthogonal Range Searching Querying a Database | 95 |
51 1Dimensional Range Searching | 96 |
52 KdTrees | 99 |
53 Range Trees | 105 |
54 HigherDimensional Range Trees | 109 |
55 General Sets of Points | 111 |
56 Fractional Cascading | 112 |
57 Notes and Comments | 115 |
58 Exercises Section | 117 |
Point Location Knowing Where You Are | 121 |
61 Point Location and Trapezoidal Maps | 122 |
62 A Randomized Incremental Algorithm | 128 |
63 Dealing with Degenerate Cases | 137 |
64 A Tail Estimate | 140 |
65 Notes and Comments | 143 |
66 Exercises | 144 |
Voronoi Diagrams The Post Office Problem | 147 |
71 Definition and Basic Properties | 148 |
72 Computing the Voronoi Diagram | 151 |
73 Notes and Comments | 160 |
74 Exercises | 162 |
Arrangements and Duality Supersampling in Ray Tracing | 165 |
81 Computing the Discrepancy | 167 |
82 Duality | 169 |
83 Arrangements of Lines | 172 |
84 Levels and Discrepancy | 177 |
85 Notes and Comments | 178 |
86 Exercises | 180 |
Delaunay Triangulations Height Interpolation | 183 |
101 Interval Trees | 212 |
102 Priority Search Trees | 218 |
103 Segment Trees | 223 |
104 Notes and Comments | 229 |
105 Exercises | 230 |
Convex Hulls Mixing Things | 235 |
111 The Complexity of Convex Hulls in 3Space | 236 |
112 Computing Convex Hulls in 3Space | 238 |
113 The Analysis | 242 |
114 Convex Hulls and HalfSpace Intersection | 245 |
115 Voronoi Diagrams Revisited | 247 |
116 Notes and Comments | 248 |
117 Exercises | 249 |
Binary Space Partitions The Painters Algorithm | 251 |
121 The Definition of BSP Trees | 253 |
122 BSP Trees and the Painters Algorithm | 255 |
123 Constructing a BSP Tree | 256 |
124 The Size of BSP Trees in 3Space | 260 |
125 Notes and Comments | 263 |
126 Exercises | 264 |
Robot Motion Planning Getting Where You Want to Be | 267 |
131 Work Space and Configuration Space | 268 |
132 A Point Robot | 270 |
133 Minkowski Sums | 275 |
134 Translational Motion Planning | 281 |
135 Motion Planning with Rotations | 283 |
136 Notes and Comments | 287 |
137 Exercises | 289 |
Quadtrees NonUniform Mesh Generation | 291 |
141 Uniform and NonUniform Meshes | 292 |
142 Quadtrees for Point Sets | 293 |
143 From Quadtrees to Meshes | 300 |
144 Notes and Comments | 302 |
145 Exercises | 304 |
Visibility Graphs Finding the Shortest Route | 307 |
151 Shortest Paths for a Point Robot | 308 |
152 Computing the Visibility Graph | 310 |
153 Shortest Paths for a Translating Polygonal Robot | 314 |
154 Notes and Comments | 315 |
155 Exercises | 316 |
Simplex Range Searching Windowing Revisited | 319 |
161 Partition Trees | 320 |
162 MultiLevel Partition Trees | 327 |
163 Cutting Trees | 330 |
164 Notes and Comments | 336 |
165 Exercises | 337 |
341 | |
359 | |
Andere Ausgaben - Alle anzeigen
Computational Geometry: Algorithms and Applications Mark de Berg,Marc van Krefeld,Mark Overmars,Otfried Cheong Eingeschränkte Leseprobe - 2013 |
Computational Geometry: Algorithms and Applications Mark de Berg,Marc Van Kreveld,Mark Overmars,Otfried Schwarzkopf Eingeschränkte Leseprobe - 2000 |
Häufige Begriffe und Wortgruppen
2-dimensional algorithm answer arrangement assume bound boundary called canonical subsets Chapter circle complexity compute configuration connected consider construct contains convex hull corresponding created data structure defined Delaunay denote described determine direction doubly-connected edge list edge endpoint event example exists expected face facets Figure geometric give given graph half-edge half-planes Hence input insertion inside interior intersection intervals leaf Lemma lies line segments linear program look means mesh Minkowski sum motion node Notes O(nlogn objects obstacles partition path planar plane planning points polygon position possible problem Proof prove quadtree query random range region reported result robot root running set of points side simple smallest solution solve space split square storage stored subdivision subtree sweep line takes Theorem trapezoids tree triangulation upper vertex vertices visible Voronoi diagram y-coordinate
Beliebte Passagen
Seite 345 - Y.-J. Chiang, FP Preparata, and R. Tamassia. A unified approach to dynamic point location, ray shooting, and shortest paths in planar maps.
Seite 343 - J.-D. Boissonnat, O. Devillers, R. Schott, M. Teillaud, and M. Yvinec. Applications of random sampling to on-line algorithms in computational geometry. Discrete Comput.
Seite iv - Department of Computer Science, Utrecht University PO Box 80.089, 3508 TB Utrecht, The Netherlands Abstract...
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