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
Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm
Pattern Recognition Letters
On the complexity of curve fitting algorithms
Journal of Complexity
Optimal multi-thresholding using a hybrid optimization approach
Pattern Recognition Letters
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
Robust threshold estimation for images with unimodal histograms
Pattern Recognition Letters
Hi-index | 0.10 |
The segmentation 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 segmentation program does not produce a satisfactory result. In this paper, we propose to use the multiobjective optimization approach to find the optimal thresholds of two criteria: the within-class criterion and the overall probability of error criterion. In addition we develop a new variant of Simulated Annealing adapted to continuous problems to solve the histogram Gaussian curve fitting problem. Six examples of test images are presented to compare the efficiency of our segmentation method, called Combination of Segmentation Objectives (CSO), based on the multiobjective optimization approach, with that of two classical competing methods: Otsu method and Gaussian curve fitting method. From the viewpoints of visualization, object size and image contrast, our experimental results show that the segmentation method based on multiobjective optimization performs better than the Otsu method and the method based on Gaussian curve fitting.