### Table 2: Time in Seconds to compute the Convex Hull

1996

"... In PAGE 4: ... The performance of these two algorithms was compared to an associative algorithm based on the Graham Scan in #5B1,2#5D. In #5B1#5D, a graph based on Table2 indicate that these algorithms haveanaverage running time of O#28log n#29 or better.... ..."

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### Table 1 Automatic parallelization of sequential convex hull algorithms Algorithm Number

2005

"... In PAGE 9: ... Consequently, the com- piler refused to generate a parallel version of these applications. Table1 summarizes the results. 5.... ..."

### Table 2: Time in Seconds to compute the Convex Hull for n points.

1996

"... In PAGE 4: ... The performance of these two algorithms was compared to an associative algorithm based on the Graham Scan in [1, 2]. In [1], a graph based on Table2 indicate that these algorithms have an average running time of O(log n) or better.... ..."

Cited by 12

### Table 1: Classi cation of intersections of a triangle from outer low-resolution mesh with the convex hull of the inner mesh

"... In PAGE 6: ... The main task lies in splitting and re- triangulating the triangles belonging to both the low-res and high-res mesh in the overlapping regions (see the top right diagram of Figure 11). Possible intersections of a low-res triangle and the con- vex hull are summarized in Table1 . There are three pos- Figure 9: Possible intersections between a low-resolution triangle and the convex hull of high-resolution mesh.... ..."

### Table 1: Properties of Convex-Hull and Convex sets.

1994

"... In PAGE 2: ... 100]), meaning that the conventional Convex-Hull is indeed a particular case of the generalized Convex-Hull. Table1 shows that some of the basic properties of the Convex-Hull and of Convex sets are naturally extended to the B-Convex-Hull operation and to B-Convex sets.... ..."

Cited by 1

### Table 1: Time to compute the Convex Hull for n

1995

"... In PAGE 4: ... The actual imple- mentation allows us to examine some factors which cannot be determined solely by theoretical analysis. The results are shown in Table1 for sets from 50 to 20000 points in size. These points were obtained by randomly generating their x and y coordinates in the range 0 to 5000.... In PAGE 4: ... These points were obtained by randomly generating their x and y coordinates in the range 0 to 5000. The result in Table1 is obtained for each input size by taking the mean of the time to compute the convex hull points from over 60 di#0Berent random generated data sets. Since there is no built in clock for the WAVETRACER, the running time for each random generated data set is obtained by tim- ing 100 executions of the algorithm and then dividing by 100.... ..."

Cited by 7

### Table 1. A comparison of update methods for different Voronoi types. The number of points is n, h is the number of points on the convex hull prior to update and k is the number of points on the convex hull following update.

"... In PAGE 27: ... The algorithmic complexity of the update methods is highly dependent on the underlying Voronoi diagram, with the OVD being the simplest and most efficient and MWVD being the most expensive. Table1... ..."

### Table 2: Performance of the Convex Hull Volume implementation.

1998

"... In PAGE 13: ... The machines used in the experiments are listed in table 1 along with their storage and speed speci cations. Table2 is copied from [ECS97] to show the performance of chD on inputs of varying degree of de- generacy. These experiments were carried out on a SparcStation 10/41 with the speci cations shown in table 1.... ..."

Cited by 23

### Table 4. Speedup of convex-hull algorithm for each iteration of Quickhull

1992

Cited by 2