# An algorithm for computing a convex hull in R*2

In an Euclidean Space the Convex Hull of a set I is defined as the intersection of all convex sets containing I.

The problem of finding convex hulls finds its practical applications in pattern recognition, image processing, statistics and geographic information system. It also serves as a tool, a building block for a number of other computational-geometric algorithms.

For planar objects, i.e., lying in the plane R2, the convex hull may be easily visualized by imagining an elastic band stretched open to encompass the given object; when released, it will assume the shape of the required convex hull.

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## Graphical example If the set I is a set of points in R2, then the convex hull is the smallest convex polygon which encloses I .

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Many convex hull algorithms had been proposed for finding the points lying on the convex polygon.

Here a dynamic function is presented that implements a gift wrapping algorithm.

It follows a suggestion from Lars Strand <Lars.Strand AT skogoglandskap DOT NONSENSE no>, written in an email to <programming AT jsoftware DOT com> at Jan 11, 2008.

A number of points in R2 are given.

The problem is to determine the smallest convex region which includes all of the points.

The solution starts with the point p0 having the smallest x-value. The next point p1 will be the point which together with the first point defines a line having the smallest angle to the x-axis. Using this point p1 as vertex, the task is to find a third point p2 so that a line from the vertex has the largest angle to the line p0-p1.

Three points in the solution are now determined.

The procedure is repeated after a shift of p1 to p0, p2 to p1, and a new point p2 is found in the same way.

The procedure stops when the new point p2 is the same as the starting point.

The solution contains the coordinates of the points of the hull.

This Dfn calls another dynamic function for computing the polar phase of all points with respect of a previously chosen point lying on the hull.

The iteration is implemented by means of primitive power operator.

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## The D function

```ConvexHull←{
⍝ ⍵: matrix (2 rows) of points - first row:abscissae  second row:ordinates
⍝ return: matrix (2 rows) of points of convex hull

⍝ try:  ConvexHull ⍉9 2⍴1 0,1 ¯10,3 3,2 1,6 3,1 6,3 1,6 1,9 0
⍝
⎕IO←0
pts←(⊂⍋⊃↓⍵)⌷⍵                        ⍝sort by x-values:first point←→start vertex
phs←{                                   ⍝ 12○⍵ polar phase ○¯1>≥○1.  see DFNS
x y←⊂[1↓⍳⍴⍴⍵]⍵                      ⍝ x and y coordinates.
x0 xn←1 0=⊂0=x                      ⍝ points on/off y axis.
}
angles←phs(0 1↓pts)-0⌷pts         ⍝phases(radians) of the vectors
ixHull←{
ix0 ix1←¯2↑⍵                        ⍝ix1: index of last point←→last vertex
v←{⍵+(⍵<0)×○2}phs pts-ix1⌷pts ⍝phases with respect to the last vertex
other←(⍳1↓⍴pts)~ix0 ix1             ⍝indexes of other points
angles←|(ix0⊃v)-(⊂other)⌷v              ⍝       _______/\_______
angles←{f←⍵>○1 ⋄ (⍵×~f)+f×-⍵-○2}angles  ⍝angles (p0-p1)  (p1-px)
⍵,(angles⍳⌈/angles)⊃other           ⍝new vertex is where angle is largest
}⍣{
0=⊃⌽⍺                               ⍝repeat until first and last vertex coincide
}ixStart
(⊂ixHull)⌷pts
}```

Author: GianluigiQuario

Studio/ConvexHull (last edited 2009-06-20 05:36:54 by KaiJaeger)