| Full text | |
| Author(s): |
Balabanian, Felipe
;
Sant'Ana da Silva, Eduardo
;
Pedrini, Helio
Total Authors: 3
|
| Document type: | Journal article |
| Source: | APPLIED ARTIFICIAL INTELLIGENCE; v. 31, n. 3, p. 197-208, 2017. |
| Web of Science Citations: | 1 |
| Abstract | |
Image thresholding is a common segmentation technique with applications in various fields, such as computer vision, pattern recognition, microscopy, remote sensing, and biology. The selection of threshold values for segmenting pixels into foreground and background regions is usually based on subjective assumptions or user judgments under empirical rules or manually determined. This work describes and evaluates six effective threshold selection strategies for image segmentation based on global optimization methods: genetic algorithms, particle swarm, simulated annealing, and pattern search. Experiments are conducted on several images to demonstrate the effectiveness of the proposed methodology. (AU) | |
| FAPESP's process: | 15/12228-1 - Detection and recognition of complex events in videos |
| Grantee: | Hélio Pedrini |
| Support Opportunities: | Scholarships abroad - Research |