Optimum Image Thresholding via Class Uncertainty and Region Homogeneity
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Performance Evaluation of Thresholding Algorithms for Optical character Recognition
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Optimal multi-thresholding using a hybrid optimization approach
Pattern Recognition Letters
A multistage adaptive thresholding method
Pattern Recognition Letters
Automatic thresholding for defect detection
Pattern Recognition Letters
A multi-level thresholding approach using a hybrid optimal estimation algorithm
Pattern Recognition Letters
IEEE Transactions on Multimedia
Image segmentation by automatic histogram thresholding
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Unsupervised range-constrained thresholding
Pattern Recognition Letters
Characteristic analysis of Otsu threshold and its applications
Pattern Recognition Letters
Modified bacterial foraging algorithm based multilevel thresholding for image segmentation
Engineering Applications of Artificial Intelligence
Fast moving target detection based on gray correlation analysis and background subtraction
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Optimal multilevel thresholding using bacterial foraging algorithm
Expert Systems with Applications: An International Journal
Image bilevel thresholding based on stable transition region set
Digital Signal Processing
Expert Systems with Applications: An International Journal
Machine Vision and Applications
Maximum similarity thresholding
Digital Signal Processing
Hi-index | 0.11 |
Otsu's method of image segmentation selects an optimum threshold by maximizing the between-class variance in a gray image. However, this method becomes very time-consuming when extended to a multi-level threshold problem due to the fact that a large number of iterations are required for computing the cumulative probability and the mean of a class. To greatly improve the efficiency of Otsu's method, a new fast algorithm called the TSMO method (Two-Stage Multithreshold Otsu method) is presented. The TSMO method outperforms Otsu's method by greatly reducing the iterations required for computing the between-class variance in an image. The experimental results show that the computational time increases exponentially for the conventional Otsu method with an average ratio of about 76. For TSMO-32, the maximum computational time is only 0.463s when the class number M increases from two to six with relative errors of less than 1% when compared to Otsu's method. The ratio of computational time of Otsu's method to TSMO-32 is rather high, up to 109,708, when six classes (M=6) in an image are used. This result indicates that the proposed method is far more efficient with an accuracy equivalent to Otsu's method. It also has the advantage of having a small variance in runtimes for different test images.