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# computational geometry introduction - PowerPoint PPT Presentation

computational geometry introduction. Mark de Berg. IPA Basic Course—Algorithms. introduction what is CG applications randomized incremental construction easy example: sorting a general framework applications to geometric problems and more …. Computational Geometry.

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## computational geometryintroduction

Mark de Berg

### IPA Basic Course—Algorithms

introduction

• what is CG

• applications

randomized incremental construction

• easy example: sorting

• a general framework

• applications to geometric problems

and more …

### Computational Geometry

Computational Geometry:

Area within algorithms research dealing with spatial data

(points, lines, polygons, circles, spheres, curves, … )

• design of efficient algorithms and data structures for spatial data

• elementary operations are assumed to take O(1) time and be available

focus on asymptotic running times of algorithms

computing intersection point of two lines, distance between two points, etc

annual rainfall

land usage

overlay

### Computational Geometry

example: compute all intersections in a set of line segments

naïve algorithm

fori= 1to n

for j =i+1 ton

doifsi intersects sj

then report intersection

Running time:O(n2)

Can we do better ?

### Computational geometry: applications (1)

geographic information systems (GIS)

Mississippi delta

Arlington

Arlington

Memphis

Memphis

Memphis

Memphis

30 cities:

430 = 1018 possibilities

### Computational geometry: applications (2)

computer-aided design and manufacturing (CAD/CAM)

• motion planning

• virtual walkthroughs

3D model of power plant (12.748.510 triangles)

9

5

4

6

8

7

3

2

1

### Computational geometry: applications (3)

3D copier

surface reconstruction

3D printer

3D scanner

### Computational geometry: applications (4)

• digitizing cultural heritage

• custom-fit products

• reverse engineering

Other applications of surface reconstruction

### Computational geometry: applications (5)

• geographic information systems

• computer-aided design and manufacturing

• robotics and motion planning

• computer graphics and virtual reality

• databases

• structural computational biology

• and more …

salary

50.000

30.000

30 45 age

### Computational Geometry: algorithmic paradigms

Deterministic algorithms

• plane sweep

• geometric divide-and-conquer

Randomized algorithms

• randomized incremental construction

• random sampling

today’s focus

### EXERCISES

• Let S be a set of n line segments in the plane.

• Design an algorithm that decides in O (n log n) time if there are any intersections in S.

Hint: Sort the endpoints on y-coordinates, and handle them in that order in a suitable manner.

• Extend the algorithm so that it reports all intersections in time O ((n+k) log n), where k is the number of intersections.

• For the experts:

• Let u (n ) be the maximum number of pairs of points at distance 1 in a set of n points in the plane. Prove that u(n) =Ω (n log n).

• Let D be a set of n discs in the plane. Prove that the union complexity of D is O (n ).

### Plane-sweep algorithm for line-segment intersection

• When two segments intersect, then there is a horizontal line L such that

• the two segments both intersect L

• they are neighbors along L

sweep line

insert segment 1

1

insert segment 2

insert segment 3

3

2

insert segment 4

insert segment 5

delete segment 1

4

5

6

Ordering along L:

1

1,2

3,1,2

3,1,4,2

3,5,1,4,2

3,5,4,2

### Plane sweep

SegmentIntersect

• Sort the endpoints by y-coordinate. Let p1,…,p2n be the sorted set of endpoints.

• Initialize empty search tree T.

• fori = 1to2n

• do { if piis the left endpoint of a segment s

• then Insert s into thetree T

• else Delete s from thetree T

• Check all pairs of segments that become

neighbors along the sweep line for intersection,

report if there is an intersection. }

Running time ?

Extension to reporting all intersections ?

COFFEE

### Lecture II: Randomized incremental construction

Warm-up exercise:

Analyze the worst-case and expected running time of the following algorithm.

ParanoidMax (A)

(* A [1..n ] is an array of n numbers *)

• Randomly permute the elements in A

• max := A[1]

• fori := 2ton

• do ifA [i ] > max

• then { max := A[i ]

• forj = 1toi -1

• do ifA[i ] > maxthen error }

• returnmax

### Worst-case running time

Worst-case = O(1) + time for lines 2—7

= O(1) + ∑ 2≤i≤n (time for i-th iteration)

= O(1) + ∑ 2≤i≤n ∑ 1≤j≤i-1 O(1)

= O(1) + ∑ 2≤i≤n O(i)

= O(n2)

### Expected running time

E [ looptijd ] = O(1) + E [ time for lines 2—7 ]

= O(1) + E [ ∑ 2≤i≤n (time for i-th iteration) ]

= O(1) + ∑ 2≤i≤n E [ time for i-th iteration ]

= O(1) + ∑ 2≤i≤n { Pr [ A[i] ≤ max { A[1..i-1 ] } ] ∙ O(1)

+ Pr [ A[i] > max { A[1..i-1 ] } ] ∙ O(i) }

= O(1) + ∑ 2≤i≤n { 1 ∙ O(1) + (1/i ) ∙ O(i) }

= O(n )

### EXERCISE

• Give an algorithm that puts the elements of a given array A[1..n] into random order. Your algorithm should be such that every possible permutation is equally likely to be the result.

### Sorting using Incremental Construction

A geometric view of sorting

Input: numbers x1,…,xn

Output: partitioning of x-axis into a collection T of empty intervals defined by the input points

x3 x1 x5 x4 x2

Incremental Construction: “Add” input points one by one,

and maintain partitioning into intervals

x3 x1 x5 x4 x2

### Sorting using Incremental Construction

• IC-Sort (S)

• T := { [ -infty : +infty] }

• fori := 1 to n

• do { Findinterval J in T that contains xi and remove it.

• Determine new intervals and insert them into T. }

How?

• for each point, maintain pointer to interval that contains it

• for each interval in T, maintain conflict list of all points inside

list: x4, x5

x3 x1 x5 x4 x2

list is empty

### Sorting using Incremental Construction

• IC-Sort (S)

• T := { [ -infty : +infty] }

• fori := 1 to n

• do { Findinterval J in T that contains xi and remove it.

• Determine new empty intervals and insert them into T.

• Split conflict list of J to get the two new conflict lists. }

• for each point, maintain pointer to interval that contains it

• for each interval in T, maintain conflict list of all points inside

Time analysis:

TOTAL TIME: O ( total size of conflict lists of all intervals that ever appear)

WORST-CASE:

O(n2)

### Sorting using Incremental Construction

• RIC-Sort (S)

• Randomly permute input points

• T := { [ -infty : +infty] }

• fori := 1 to n

• do { Findinterval J in T that contains xi and remove it.

• Determine new empty intervals and insert them into T.

• Split conflict list of J to get the two new conflict lists. }

• for each point, maintain pointer to interval that contains it

• for each interval in T, maintain conflict list of all points inside

Time analysis:

TOTAL TIME: O ( total size of conflict lists of all intervals that are created)

EXPECTED TIME: ??

### An abstract framework for RIC

S = set of n (geometric) objects; the input

C= set of configurations

D: assigns a defining set D(Δ) S to every configuration Δє C

K: assigns a killing set K(Δ) S to every configuration ΔєC

4-tuple (S, C, D, K) is called a configuration space

configurationΔє C is active over a subset S’ S if:

D(Δ) S’ and K(Δ) S’ = o

T(S) = active configurations over S; the output

/

### Sorting using Incremental Construction

• RIC(S)

• Compute random permutation x1,…xn of input elements

• T := { active configurations over empty set }

• fori := 1 to n

• do { Removeall configurations Δ with xi є K(Δ) from T.

• Determine new active configurations; insert them into T.

• Construct conflict lists of new active configurations. }

• for each xi, maintain pointers to all configurations Δ such that xi є K(Δ)

• for each configuration Δ in T, maintain conflict list of all xi є K(Δ)

Theorem: Expected total size of conflict lists of all created configs:

O( ∑ 1≤i≤n(n / i2) ∙ E [ # active configurations in step i ])

### EXERCISE

• Use the theorem to prove that the running time of the RIC algorithm for sorting runs in O( n log n ) time.

LUNCH

### Lecture III

RIC: more applications

Voronoi diagrams and Delaunay triangulations

### Principia Philosophiae (R. Descartes, 1644).

The universe according to Descartes

Voronoi diagram

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1163

1180

1153

1108

1127

981

1103

1098

1098

Construction of 3D height models (1)

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1163

1153

1180

1108

1127

1103

981

1098

1098

height?

### Construction of 3D height models (3)

Better: use interpolation to determine the height

983

983

triangulation

1163

1163

1153

1153

1180

1180

1108

1108

1127

1127

1103

1103

981

981

1098

1098

1098

1098

### Construction of 3D height models (4)

What is a good triangulation?

long and thin triangles are bad

 try to avoid small angles

### Construction of 3D height models (5)

Voronoi diagram

Delaunay triangulation:

triangulation that maximizes the minimum angle !

### Voronoi diagrams and Delaunay triangulations

P : n points (sites) in the plane

V(pi ) = Voronoi cell of site pi

= { qєR2 : dist(q,pi ) < dist(q,pj ) for all j≠i }

Vor(P ) = Voronoi diagram of P

= subdivision induced by Voronoi cells

DT(P) = Delaunay triangulation of P

= dual graph of Vor(P)

### EXERCISES

• Prove: a triangle pipjpk is a triangle in DT(P) if and only if its circumcircle C (pipjpk ) does not contain any other site

• Prove: number of triangles in DT(P) is at most2n-5

Hint: Euler’s formula for connected planar graphs:

#vertices — #edges + #faces = 2

• Design and analyze a randomized incremental algorithm to construct DT(P).

COFFEE

### RIC – Limitations of the framework

Lecture IV: wrap-up

Robot motion planning

What is the region that is reachable for the robot?

### RIC – Limitations of the framework (cont’d)

The single-cell problem:

Compute cell containing the origin

Idea: compute vertical decomposition for the cell using RIC ?!

### Spatial data structures

more computational geometry

• store geometric datain 2-, 3- or higher dimensional space

such that certain queries can be answered efficiently

• applications: GIS, graphics and virtual reality, …

Examples:

• point location

• range searching

• nearest-neighbor searching

and more … and more …

• plane sweep

• parametric search

• arrangements

• geometric duality, inversion, Plücker coordinates, …

• kinetic data structures

• core sets

• Davenport-Schinzel sequences