Convex Hull Algorithms

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Convex Hull Algorithms

  1. 1. Convex Hull Algorithms Kasun Ranga Wijeweera (krw19870829@gmail.com)
  2. 2. Formal Definitions (Convex Set)• A set S is convex if x in S and y in S implies that the segment xy is a subset of S• Example in 2D:
  3. 3. Formal Definitions (Convex Hull of a Set of Points)• The convex hull of a set S of points is the smallest convex set containing all the points in S• Example in 2D:
  4. 4. Intuitive Appreciations (Convex Hull of a Set of Points in 2D)• The convex hull of a set of points in two dimensions is the shape taken by a rubber band stretched around nails pounded into the plane at each point• Example:
  5. 5. Intuitive Appreciations (Convex Hull of a Set of Points in 3D)• The boundary of the convex hull of points in three dimensions is the shape taken by plastic wrap stretched tightly around the points• Example:
  6. 6. Applications• Collision Avoidance
  7. 7. Applications• Minimum Bounding Box
  8. 8. Applications• Shape Analysis
  9. 9. Existing Convex Hull Algorithms• Gift Wrapping (1970)• Graham Scan (1972)• Quick Hull (1977)• Divide and Conquer (1977)• Monotone Chain (1979)• Incremental (1984)• Marriage before Conquest (1986)• Chan (1996)
  10. 10. Interior Points AlgorithmBased on the following Lemma A point is non-extreme if and only if it is inside some (closed) triangle whose vertices are points of the set and is not itself a corner of that triangle
  11. 11. Interior Points AlgorithmAlgorithm: INTERIOR POINTSfor each i do for each j != i do for each k != i != j do for each l != k != i != j do if p(l) in Triangle{ p(i), p(j), p(k) } then p(l) is non-extreme
  12. 12. Any Questions?
  13. 13. Thank You!

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