A fast convex hull algorithm with maximum inscribed circle affine transformation

Authors:
Runzong Liu;Bin Fang;Yuan Yan Tang;Jing Wen;Jiye Qian
Affiliations:
College of Computer Science, Chongqing University, Chongqing 400030, PR China;College of Computer Science, Chongqing University, Chongqing 400030, PR China;College of Computer Science, Chongqing University, Chongqing 400030, PR China;College of Computer Science, Chongqing University, Chongqing 400030, PR China;College of Computer Science, Chongqing University, Chongqing 400030, PR China
Venue:
Neurocomputing
Year:
2012

Citing 10
Cited 1

Computational geometry: an introduction

Computational geometry: an introduction
Visual attention

Foundations of cognitive science
The quickhull algorithm for convex hulls

ACM Transactions on Mathematical Software (TOMS)
Adaptive Normalization of Handwritten Characters Using Global/Local Affine Transformation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet Approximation-Based Affine Invariant Shape Representation Functions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing Convex Hulls by Automata Iteration

CIAA '08 Proceedings of the 13th international conference on Implementation and Applications of Automata
The Multi-objective Differential Evolution Algorithm Based on Quick Convex Hull Algorithms

ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 04
Kernel subclass convex hull sample selection method for SVM on face recognition

Neurocomputing
Short Communication to SMI 2011: Efficient convex hull computation for planar freeform curves

Computers and Graphics
Image registration and object recognition using affine invariants and convex hulls

IEEE Transactions on Image Processing

Letters: An enhanced version and an incremental learning version of visual-attention-imitation convex hull algorithm

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents a fast convex hull algorithm for a large point set. The algorithm imitates the procedure of human visual attention derived in a psychological experiment. The merit of human visual attention is to neglect most inner points directly. The proposed algorithm achieves a significant saving in time and space in comparison with the two best convex hull algorithms mentioned in a latest review proposed by Chadnov and Skvortsov in 2004. Furthermore, we propose to use an affine transformation to solve the narrow shape problem for computing the convex hull faster.