2D Convex Hulls and Extreme Points

*Susan Hert and Stefan Schirra*

11.1 | Introduction | ||||

11.2 | Convex Hull | ||||

11.3 | Example using Graham-Andrew's Algorithm | ||||

11.4 | Extreme Points and Hull Subsequences | ||||

11.5 | Traits Classes | ||||

11.6 | Convexity Checking |

A subset $$*S ^{2}* is convex if for any two points $$

This chapter describes the functions provided in CGAL for producing convex hulls in two dimensions as well as functions for checking if sets of points are strongly convex are not. There are also a number of functions described for computing particular extreme points and subsequences of hull points, such as the lower and upper hull of a set of points.

Each of the convex hull functions presents the same interface to the
user. That is, the user provides a pair of iterators, *first*
and *beyond*, an output iterator *result*, and a traits class
*traits*. The points in the range [*first*, *beyond*) define
the input points whose convex hull is to be computed. The counterclockwise
sequence of extreme points is written to the sequence starting at position
*result*, and the past-the-end iterator for the resulting set of
points is returned. The traits classes for the functions specify the types
of the input points and the geometric primitives that are required by
the algorithms. All functions provide an interface in which this
class need not be specified and defaults to types and operations defined
in the kernel in which the input point type is defined.

Given a sequence of $$*n* input points with $$*h* extreme points,
the function *convex_hull_2*
uses either the output-sensitive $$*O(n h)* algorithm of Bykat [Byk78]
(a non-recursive version of the quickhull [BDH96] algorithm)
or the algorithm of Akl and Toussaint, which requires $$*O(n *log$$*n)* time
in the worst case. The algorithm chosen depends on the kind of
iterator used to specify the input points. These two algorithms are
also available via the functions *ch_bykat* and *ch_akl_toussaint*,
respectively. Also available are
the $$*O(n *log$$*n)* Graham-Andrew scan algorithm [And79, Meh84]
(*ch_graham_andrew*
),
the $$*O(n h)* Jarvis march algorithm [Jar73]
(*ch_jarvis*
),
and Eddy's $$*O(n h)* algorithm [Edd77]
(*ch_eddy*
), which corresponds to the
two-dimensional version of the quickhull algorithm.
The linear-time algorithm of Melkman for producing the convex hull of
simple polygonal chains (or polygons) is available through the function
*ch_melkman*
.

In the following example a convex hull is constructed from point data read
from standard input using *Graham_Andrew* algorithm. The resulting convex
polygon is shown at the standard output console. The same results could be
achieved by substituting the function *CGAL::ch_graham_andrew* by other
function like *CGAL::ch_bykat*.

File:examples/Convex_hull_2/ch_from_cin_to_cout.cpp

#include <CGAL/Exact_predicates_inexact_constructions_kernel.h> #include <CGAL/ch_graham_andrew.h> typedef CGAL::Exact_predicates_inexact_constructions_kernel K; typedef K::Point_2 Point_2; int main() { CGAL::set_ascii_mode(std::cin); CGAL::set_ascii_mode(std::cout); std::istream_iterator< Point_2 > in_start( std::cin ); std::istream_iterator< Point_2 > in_end; std::ostream_iterator< Point_2 > out( std::cout, "\n" ); CGAL::ch_graham_andrew( in_start, in_end, out ); return 0; }

There are also functions available for computing certain subsequences
of the sequence of extreme points on the convex hull. The function
*ch_jarvis_march*
generates the counterclockwise ordered subsequence of
extreme points between a given pair of points and
*ch_graham_andrew_scan*
computes the sorted sequence of extreme points that are
not left of the line defined by the first and last input points.

Finally, a set of functions
(*ch_nswe_point*, *ch_ns_point*, *ch_we_point*, *ch_n_point*,
*ch_s_point*, *ch_w_point*, *ch_e_point*)
is provided for computing extreme points of a
2D point set in the coordinate directions.

Each of the functions used to compute convex hulls or extreme points
is parameterized by a traits class, which specifies the types and geometric
primitives to be used in the computation. There are several implementations
of 2D traits classes provided in the library. The class
*Convex_hull_traits_2<R>*
corresponds to the default traits class that provides the types and
predicates presented in the 2-dimensional CGAL kernel in which the input
points lie. The class
*Convex_hull_constructive_traits<R>*
is a second traits class based on CGAL primitives but differs from
*Convex_hull_traits_2* in that some of its primitives reuse
intermediate results to speed up computation.
In addition, there are three projective traits classes
(*Convex_hull_projective_xy_traits_2*,
*Convex_hull_projective_xz_traits_2*, and
*Convex_hull_projective_yz_traits_2*),
which may be used to compute the convex hull of a set of three-dimensional
points projected into each of the three coordinate planes.

The functions *is_ccw_strongly_convex_2* and *is_cw_strongly_convex_2*
check whether a given sequence of 2D points forms a (counter)clockwise strongly
convex polygon.
. These are used in postcondition
testing of the two-dimensional convex hull functions
.