A novel auto-parameters selection process for image segmentation
- Publication Type:
- Conference Proceeding
- Citation:
- 2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012
- Issue Date:
- 2012-10-04
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
 | 2012001202OK.pdf | 1.94 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Segmentation is a process to obtain the desirable features in image processing. However, the existing techniques that use the multilevel thresholding method in image segmentation are computationally demanding due to the lack of an automatic parameter selection process. This paper proposes an automatic parameter selection technique called an automatic multilevel thresholding algorithm using stratified sampling and Tabu Search (AMTSSTS) to remedy the limitations. It automatically determines the appropriate threshold number and values by (1) dividing an image into even strata (blocks) to extract samples; (2) applying a Tabu Search-based optimization technique on these samples to maximize the ratios of their means and variances; (3) preliminarily determining the threshold number and values based on the optimized samples; and (4) further optimizing these samples using a novel local criterion function that combines with the property of local continuity of an image. Experiments on Berkeley datasets show that AMTSSTS is an efficient and effective technique which can provide smoother results than several developed methods in recent years. © 2012 IEEE.
Please use this identifier to cite or link to this item: