A fast sequential method for polygonal approximation of digitized curves
Computer Vision, Graphics, and Image Processing
On the Detection of Dominant Points on Digital Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric dominant point detection
Pattern Recognition
Detection of significant points and polygonal approximation of digitized curves
Pattern Recognition Letters
An algorithm for detection of dominant points and polygonal approximation of digitized curves
Pattern Recognition Letters
Pattern Recognition Letters
Optimum polygonal approximation of digitized curves
Pattern Recognition Letters
A new split-and-merge technique for polygonal approximation of chain coded curves
Pattern Recognition Letters
Techniques for Assessing Polygonal Approximations of Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new method for polygonal approximation using genetic algorithms
Pattern Recognition Letters
A boundary concavity code to support dominant point detection
Pattern Recognition Letters
On Critical Point Detection of Digital Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polygon Evolution by Vertex Deletion
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Modelling competitive Hopfield networks for the maximum clique problem
Computers and Operations Research
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Optimizing neural networks on SIMD parallel computers
Parallel Computing
A novel approach to polygonal approximation of digital curves
Journal of Visual Communication and Image Representation
A neural model for the p-median problem
Computers and Operations Research
Optimized polygonal approximation by dominant point deletion
Pattern Recognition
Dominant point detection by reverse polygonization of digital curves
Image and Vision Computing
Neural techniques for combinatorial optimization with applications
IEEE Transactions on Neural Networks
Design and analysis of maximum Hopfield networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
| Hi-index | 12.05 |
The polygonal approximation is an important topic in the area of pattern recognition, computer graphics and computer vision. This paper firstly proposes a new computational energy function to properly express the objective of the polygonal approximation problem based on competitive Hopfield neural network (CHNN), and then proposes a stochastic CHNN (SCHNN) by introducing stochastic dynamics into the CHNN to help the network escape from local minima. In order to further improve the performance of the SCHNN, a multi-start strategy or re-start mechanism is introduced. The multi-start strategy or re-start mechanism super-imposed on the SCHNN is characterized by alternating phases of cooling and reheating the stochastic dynamics, thus provides a means to achieve an effective dynamic or oscillating balance between intensification and diversification during the search. The proposed multi-start SCHNN (MS-SCHNN) is tested on a set of benchmark problems and several large size test instances. Simulation results show that the proposed MS-SCHNN is better than or competitive with several typical neural network algorithms such as CHNN and transiently chaotic neural network, metaheuristic algorithms such as genetic algorithms, and 12 commonly referred state-of-the-art algorithms specifically developed for the polygonal approximation. Furthermore, the chain codes and results of the proposed algorithm for the large size curves are also provided.