Kinetic Convex Hull Algorithm Using Spiral Kinetic Data Structure

2007 ◽  
Author(s):  
Ali Sajedi Badashian ◽  
Mohammad Reza Razzazi
Keyword(s):  
Data Structure ◽  
Convex Hull ◽  
Kinetic Data ◽  
Kinetic Data Structure ◽  
Convex Hull Algorithm

scholarly journals Agglomerative Clustering of Growing Squares

Algorithmica ◽  
2021 ◽  
Author(s):  
Thom Castermans ◽  
Bettina Speckmann ◽  
Frank Staals ◽  
Kevin Verbeek
Keyword(s):  
Data Structure ◽  
Kinetic Data ◽  
Time Algorithm ◽  
Agglomerative Clustering ◽  
Worst Case ◽  
Kinetic Data Structure ◽  
Clustering Problem ◽  
Interactive Map

AbstractWe study an agglomerative clustering problem motivated by interactive glyphs in geo-visualization. Consider a set of disjoint square glyphs on an interactive map. When the user zooms out, the glyphs grow in size relative to the map, possibly with different speeds. When two glyphs intersect, we wish to replace them by a new glyph that captures the information of the intersecting glyphs. We present a fully dynamic kinetic data structure that maintains a set of n disjoint growing squares. Our data structure uses $$O\bigl (n \log n \log \log n\bigr )$$ O ( n log n log log n ) space, supports queries in worst case $$O\bigl (\log ^2 n\bigr )$$ O ( log 2 n ) time, and updates in $$O\bigl (\log ^5 n\bigr )$$ O ( log 5 n ) amortized time. This leads to an $$O\bigl (n\,\alpha (n)\log ^5 n\bigr )$$ O ( n α ( n ) log 5 n ) time algorithm to solve the agglomerative clustering problem. This is a significant improvement over the current best $$O\bigl (n^2\bigr )$$ O ( n 2 ) time algorithms.

scholarly journals A soft kinetic data structure for lesion border detection

2010 ◽  
Vol 26 (12) ◽  
pp. i21-i28 ◽  
Author(s):  
S. Kockara ◽  
M. Mete ◽  
V. Yip ◽  
B. Lee ◽  
K. Aydin
Keyword(s):  
Data Structure ◽  
Kinetic Data ◽  
Border Detection ◽  
Kinetic Data Structure

KINETIC COLLISION DETECTION FOR SIMPLE POLYGONS

2002 ◽  
Vol 12 (01n02) ◽  
pp. 3-27 ◽  
Author(s):  
DAVID KIRKPATRICK ◽  
JACK SNOEYINK ◽  
BETTINA SPECKMANN
Keyword(s):  
Data Structure ◽  
Collision Detection ◽  
Kinetic Data ◽  
Minimum Size ◽  
Active Set ◽  
Worst Case ◽  
Kinetic Data Structure ◽  
External Events ◽  
Definition Of ◽  
Case Number

We design a simple and elegant kinetic data structure for detecting collisions between polygonal (but not necessarily convex) objects in motion in the plane. Our structure is compact, maintaining an active set of certificates whose number is proportional to a minimum-size set of separating polygons for the objects. It is also responsive; on the failure of a certificate invariants can be restored in time logarithmic in the total number of object vertices. It is difficult to characterize the efficiency of our structure for lack of a canonical definition of external events. Nevertheless we give an easy upper bound on the worst case number of certificate failures.

A Randomized Parallel Three-Dimensional Convex Hull Algorithm for Coarse-Grained Multicomputers

1997 ◽  
Vol 30 (6) ◽  
pp. 547-558 ◽  
Author(s):  
F. Dehne ◽  
X. Deng ◽  
P. Dymond ◽  
A. Fabri ◽  
A. A. Khokhar
Keyword(s):  
Convex Hull ◽  
Three Dimensional ◽  
Coarse Grained ◽  
Convex Hull Algorithm

scholarly journals Improved Convex Hull Algorithm Applied to Body Size Measurements

2021 ◽  
Vol 1790 (1) ◽  
pp. 012089
Author(s):  
Fang Qi ◽  
Sun GuangWu ◽  
Chen Yu
Keyword(s):  
Body Size ◽  
Convex Hull ◽  
Convex Hull Algorithm

scholarly journals Method for Vortex Shape Retrieval and Area Calculation Based on Convex Hull Algorithm

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 2706-2714
Author(s):  
Xu Wei ◽  
Jiyu Li ◽  
Bo Long ◽  
Xiaodan Hu ◽  
Han Wu ◽  
...  
Keyword(s):  
Convex Hull ◽  
Shape Retrieval ◽  
Convex Hull Algorithm

A Convex Hull Algorithm for Minimization of Gibbs Energy in Two-Phase Multicomponent Alloys

2011 ◽  
Vol 172-174 ◽  
pp. 1214-1219
Author(s):  
Nataliya Perevoshchikova ◽  
Benoît Appolaire ◽  
Julien Teixeira ◽  
Sabine Denis
Keyword(s):  
Gibbs Energy ◽  
Convex Hull ◽  
Large Temperature ◽  
Two Phase ◽  
Multicomponent Alloys ◽  
General Dimension ◽  
Newton Raphson ◽  
Convex Hull Algorithm ◽  
Energy Hypersurface ◽  
Equilibrium Calculations

We have adapted the Quickhull algorithm with the general dimension Beneath-Beyondalgorithm [6] for computing the convex hull of the Gibbs energy hypersurface of multicomponenttwo-phase alloys. We illustrate the salient features of our method with calculations of isothermalferrite-austenite equilibria in Fe-C-Cr. Finally, successive equilibrium calculations in a Fe-C-Cr-Mosteel over a large temperature range show the benefit of computing the convex hull before performingthe conventional Newton-Raphson search.

A fast convex hull algorithm inspired by human visual perception

2018 ◽  
Vol 77 (23) ◽  
pp. 31221-31237 ◽  
Author(s):  
Runzong Liu ◽  
Yuan Yan Tang ◽  
Patrick P. K. Chan
Keyword(s):  
Visual Perception ◽  
Convex Hull ◽  
Human Visual Perception ◽  
Convex Hull Algorithm
Sign in / Sign up

Export Citation Format

Share Document