Pattern Recognition
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
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
Optimal thresholding—a new approach
Pattern Recognition Letters
Enhanced simulated annealing for globally minimizing functions of many-continuous variables
ACM Transactions on Mathematical Software (TOMS)
Quantitative evaluation of color image segmentation results
Pattern Recognition Letters
Multicriterion Optimisation in Engineering
Multicriterion Optimisation in Engineering
Digital Image Processing Algorithms and Applications
Digital Image Processing Algorithms and Applications
On the complexity of curve fitting algorithms
Journal of Complexity
Optimal multi-thresholding using a hybrid optimization approach
Pattern Recognition Letters
Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy
Pattern Recognition Letters
Automatic thresholding for defect detection
Pattern Recognition Letters
Adaptive thresholding by variational method
IEEE Transactions on Image Processing
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
Fractional differentiation and non-Pareto multiobjective optimization for image thresholding
Engineering Applications of Artificial Intelligence
Image Thresholding Using TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm
Learning and Intelligent Optimization
Computational applications of nonextensive statistical mechanics
Journal of Computational and Applied Mathematics
A thresholding method based on two-dimensional fractional differentiation
Image and Vision Computing
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Medical image thresholding using online trained neural networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Multi-level image thresholding by synergetic differential evolution
Applied Soft Computing
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The thresholding process based on the optimization of one criterion only does not work well for a lot of images. In many cases, even when equipped with the optimal value of the threshold of its single criterion, the thresholding program does not produce a satisfactory result. In this paper, we propose to use the multiobjective optimization approach to find the optimal thresholds of three criteria: the within-class criterion, the entropy and the overall probability of error criterion. In addition we develop a new variant of simulated annealing adapted to continuous problems to solve the Gaussian curve-fitting problem. Some examples of test images are presented to compare our segmentation method, based on the multiobjective optimization approach, with that of four competing methods: Otsu method, Gaussian curve fitting-based method, valley-emphasis-based method and two-dimensional Tsallis entropy-based method. From the viewpoints of visualization, object size and image contrast, our experimental results show that the thresholding method based on multiobjective optimization performs better than the competing methods.