Pattern Recognition
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
An iterative algorithm for minimum cross entropy thresholding
Pattern Recognition Letters
Optimal multi-thresholding using a hybrid optimization approach
Pattern Recognition Letters
A novel image thresholding method based on Parzen window estimate
Pattern Recognition
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Multilevel Minimum Cross Entropy Threshold Selection Based on Quantum Particle Swarm Optimization
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02
Multilevel thresholding for image segmentation through a fast statistical recursive algorithm
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Automatic Threshold Selection Based on Particle Swarm Optimization Algorithm
ICICTA '08 Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation - Volume 01
Pattern Recognition Letters
Image vector quantization algorithm via honey bee mating optimization
Expert Systems with Applications: An International Journal
Multilevel minimum cross entropy threshold selection based on the firefly algorithm
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Image thresholding is an important technique for image processing and pattern recognition. Many thresholding techniques have been proposed in the literature. Among them, the minimum cross entropy thresholding (MCET) has been widely applied. In this paper, a new multilevel MCET algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. Three different methods included the exhaustive search, the particle swarm optimization (PSO) and the quantum particle swarm optimization (QPSO) methods are also implemented for comparison with the results of the proposed method. The experimental results manifest that the proposed HBMO-based MCET algorithm can efficiently search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. In comparison with the other two thresholding methods, the segmentation results using the HBMO-based MCET algorithm is the best. Furthermore, the convergence of the HBMO-based MCET algorithm can rapidly achieve, and the results are validated that the proposed HBMO-based MCET algorithm is efficient.