The construction of planar convex hull is one of the most fundamental problems in computational geometry. The applications of convex hull spread over large number of fields including pattern recognition, regression, collision detection, area estimation, spectrometry, topology, etc. For instance, computer animation, the most crucial section of computer gaming, requires fast approximation for real-time response. Consequently, it is evidential from literature that numerous studies focus on fast approximation of different geometric structures in computer graphics [1,2]. Moreover, the construction of exact and approximate convex hull is used as a preprocessing or intermediate step to solve many problems in computer graphics [3,4].

Convex hull for a given finite set of points where denotes the -dimensional Euclidean space, is defined as the smallest convex set that contains all the points. A set is convex if for two arbitrary points, the line segment is entirely contained in the set. Alternatively, the convex hull can be defined as the intersection of all halfspaces (or half-planes in) containing. The main focus of this article is limited on the convex hull in Euclidean plane.

Because of the importance of convex hull, it is natural to study for improvement of running time and storage requirements of the convex hull algorithms in different Euclidean spaces. Graham [5] published one of the fundamental algorithms of convex hull, widely known as Graham’s scan as early as 1972. This is one of the earliest convex hull algorithms with worst-case running time. Graham’s algorithm is asymptotically optimal since is the lower bound of planar convex hull problem. It can be shown [6] that is a lower bound of a similar but weaker problem of determining the points belonging to the convex hull, not necessarily producing them in cyclic order.

However, all of these lower bound arguments assume that the number of hull vertices is at least a fraction of. Another algorithm due to Jarvis [7] surpasses the Graham’s scan algorithm if the number of hull vertices is substantially smaller than. This algorithm with running time is known as Jarvis’s march. There is a strong relation between sorting algorithm and convex hull algorithm in the plane. Several divide-and-conquer algorithms including MergeHull and QuickHull algorithms of convex hull modeled after the sorting algorithms [8] and the first algorithm Graham’s [5] scan uses explicit sorting of points.

In 1986, Kirkpatrick and Seidel [9] proposed an algorithm that computes the convex hull of a set of points in the plane in time. Their algorithm is both output sensitive and worst case optimal. Later, a simplification of this algorithm [9] was obtained by Chan [10]. In the following year Melkman [11] presented a simple and elegant algorithm to construct the convex hull for simple polyline. This is one of the on-line algorithms which construct the convex hull in linear time.

Approximation algorithms for convex hull are useful for applications including area estimation of complex shapes that require rapid solutions, even at the expense of accuracy of constructed convex hull. Based on approximation output, these algorithms of convex hull could be divided into three groups—near, inner, and outer approximation algorithms. Near, inner, and outer approximation algorithms compute near, inner, and outer approximation of the exact convex hull for some point set respectively.

In 1982, Bentley et al. [12] published an approximation algorithm for convex hull construction with running time. Another algorithm due to Soisalon-Soininen [13] which uses a modified approximation scheme of  [12] and has the same running time and error bound. Both of the algorithms are the inner approximation of convex hull algorithm. The proposed algorithm in this article is a near approximation algorithm of running time.

Let be the finite set of points in general position and the (accurate) convex hull of be. Kavan, Kolingerova, and Zara [14] proposed an algorithm with running time which partitions the plane into sectors centered in the origin. Their algorithm requires the origin to be inside the convex hull. (It is possible to choose a point and translate all the other points of accordingly using additional steps in their algorithm). Conversely, we partition the plane into vertical sector pair with equal central angle in the origin and for our algorithm the origin could be located outside of the convex hull. The sets represent the vertically opposite sectors that form the vertical sector pairs defined as

where, and the central angle. Then, the sets and denote the points belonging to the set in sectors and respectively. Formally,

A pair of unit vectors and obtain in th vertical sector pair as

The maximum projection magnitudes in the directions of and are (Figure 1)

The definition of max function is extend to return for no parameter. The sets of points which provide the maximum projection magnitude in the sectors of th vertical sector pair are

The vectors that produce the maximum magnitude in the directions of and for some points in the th vertical sector pair are

The magnitude of the vectors and could be for the th vertical sector pair containing less than two points. The sets and containing all the finite vectors in the ranges and, are

Let, and contains at least three terminal points of the vectors in general position to construct the convex hull.

The convex hull approximation of vertical sector pairs according to the proposed algorithm in this article is:

The input of the algorithm is a set of points in general position. For simplicity, we assume that the origin and. (This assumption can be achieved by taking a point arbitrarily close to the origin instead of the origin itself, within the upper bound of error calculated in Section 5) (Figure 2).

We also assume that at least two vertical sector pairs together contains minimum three points (where none of these two are empty). The assumption can be reduced to one of the requirements of minimum three points input (i.e.,) of convex hull. To illustrate that, let us consider and to be two points in such that where is the origin. Such two points do exist if no three points are collinear in (i.e., the points of are in general position). If is the bisector of, then adding the angle of from positive -axis as an offset to every vertical sector pair ensures that all the input points cannot be in the same vertical sector pair. Thus, the assumption is satisfied. Alternatively, if less than three absolute values in are finite, then for each, assign to and where these are infinite. (The next pa-

ragraph contains details about M.) Therefore, the number of points in must be at least three.

A circular array is used to contain the pairs of unit vectors of all the vertical sector pairs and another circular array is used to hold the number of pairs of maximum projection magnitude in all the vertical sector pairs. Both circular arrays have the same size of and use zero based indexing scheme. The function atan2 is a variation of function arctan with point as a parameter. The function returns the angle in radians between the point and the positive -axis of the plane in the range of. The function anglex searches sequentially for the index of maximum angular distance between two consecutive positive finite vectors (computed using projection magnitude with index referring angle). If the index is such that maximum angle occurs in between and, the anglex function returns. The final convex hull is constructed using Melkman’s [11] algorithm from set of points which are the terminal points of finite vectors computed in steps 14 and 15. If the first three points of are collinear, displacing one of these points within the error bound solves the problem.

Since the vertices of the convex hull produced by the proposed algorithm are not necessarily in the input point set, the algorithm cannot be applied straight away to solve some other problems. Let us consider another circular array of size which used to contain the points generating the inner products of. Adding the point instead of to the sequence in Steps 14 and 15 ensures that the vertices of the convex hull will be the points from. These modifications of the algorithm allow us to solve some problems including approximate farthest-pair problem but increase the upper bound of error (described in Section 5) to.

There are different schemes for measuring the error of an approximation of the convex hull. We measured the error as distance from point set of accurate convex hull. The distance of an arbitrary point from a set is defined as

Formally the approximation error can be defined as

It is sufficient to determine the upper bound of error of the approximate convex hull. Let, be be a point lying outside of the convex hull and be the origin. Suppose that, is an edge of the approximate convex hull (as shown in Figure 3).

Therefore, the distance of the point from the

is

.

The distance of the point from vertex is. Thus,

.

Let, and. Thus we obtain

It follows that the minimum distance directly depends on which is denoted as function. Thus, the upper bound of approximation error is. If approaches to infinity, the converges to.

For instance, when k approaches to a large value, the area approximation error of the circle is reducing exponentially as shown in Figure 4. Therefore, this algorithm is more optimize than the KKZ algorithm [14] with respect to error bound as shown in Figure 5.

Theorem 1. The approximation algorithm produces the convex hull from a set of points in correctly within the prescribed error bound.

Proof. Since, Melkman’s algorithm can construct the convex hull correctly for points on a simple polygonal chain, it suffices to prove that the sequence of points denotes a simple polygonal chain. (Melkman [11] published the on-line algorithm of convex hull with formal proof of correctness in 1987).

Suppose that, the plane is partitioned into vertical sector pairs which correspond to the sequence

of simple sectors. The sequence

of sectors is ordered according to the angle measured anticlockwise. If is a half-line (denoting the set of points on the half-line) from the origin in the direction of the unit vector of the sector, then the sequence

represents all the half-lines correlated with the sequence. According to the algorithm all the points of must be distinct (as referred in steps 9-10) and lying on some of the half-lines of. The sequence of half-lines where each contained at least one point from, is

Each half-line can contain at most two points of. Let, are points on each halfline such that. If a half-line

contains only one point of, the length of virtual is zero with and refer to the same point of (e.g., contains only one point in the Figure 6). Let denotes the angle from to where and are half-lines from the origin. Since the angle between two consecutive half-lines and (because for our assumptions and in the algorithm), no two line segments and intersect each other, for all. However, the line segment could cross the polygonal chain if the angle. The equation of (derived using the law of sines and basic properties of triangle) also illustrates this fact mathematically for (as shown in the Figure 6).

The solution with minimum magnitude of the above equation is negative for, even if. Thus the line segment could intersect with the edges of the polygonal chain only if. If the maximum angle between two consecutive half-lines is for some, then anglex function returns the index that ensures the construction a simple polygonal chain where and all the indices are modulo. Thus the sequence of points represents a simple polygonal chain. (It is possible to prove the algorithm obtained by interchanging the steps 14 and 15, using a similar method).

Theorem 2. If is the number of input points and is the number of vertical sector pairs in, then the running time of the proposed algorithm is.

Proof. Let us estimate the running time for each part of the algorithm to prove that the algorithm compute the approximate convex hull in time. It is clear that, the initialization steps 2-5 take time. Since, the next loop of steps 6-10 iterates for each point, thus it takes time considering constant time for floor function. According to the description of anglex function in Section 4, the function can be implemented in time because it requires iterations to compute the index. The loop of steps 13-15 takes time and Melkman’s [11] algorithm runs in linear time. Steps 1 and 11 require constant time. Thus the running time of the algorithm is.

Geometric algorithms are frequently formulated under the non-degeneracy assumption or general position assumption [15] and the proposed algorithm in this article is also not an exception. To make the implementation of the algorithm robust an integrated treatment for the special cases can be applied. There are other general techniques called perturbation schemes [16,17] to transform the input into general position and allow the algorithm to solve the problem on perturbed input. Both symbolic perturbation and numerical (approximation) perturbation (where perturbation error is consistent with the error bound of the algorithm) can be used on the points of to eliminate degenerate cases.

This research is supported by a grant from Independent University Bangladesh (IUB).

The article describes a near approximation algorithm for convex hull however it is possible to extend the concept for inner as well as outer approximation algorithms for convex hull. An illustration of inner approximate convex hull algorithm is shown in Figure 7.