Image Thresholding Improved by Global Optimization Methods
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 subject...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2017-03-01
|
| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2017.1300050 |
| _version_ | 1797684900639277056 |
|---|---|
| author | Felipe Balabanian Eduardo Sant'Ana da Silva Helio Pedrini |
| author_facet | Felipe Balabanian Eduardo Sant'Ana da Silva Helio Pedrini |
| author_sort | Felipe Balabanian |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T00:37:27Z |
| format | Article |
| id | doaj.art-bbb2f0af5da04f70aaad1879623c4dae |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:37:27Z |
| publishDate | 2017-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-bbb2f0af5da04f70aaad1879623c4dae2023-09-15T09:33:55ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452017-03-0131319720810.1080/08839514.2017.13000501300050Image Thresholding Improved by Global Optimization MethodsFelipe Balabanian0Eduardo Sant'Ana da Silva1Helio Pedrini2University of CampinasUniversity of CampinasUniversity of CampinasImage 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.http://dx.doi.org/10.1080/08839514.2017.1300050 |
| spellingShingle | Felipe Balabanian Eduardo Sant'Ana da Silva Helio Pedrini Image Thresholding Improved by Global Optimization Methods Applied Artificial Intelligence |
| title | Image Thresholding Improved by Global Optimization Methods |
| title_full | Image Thresholding Improved by Global Optimization Methods |
| title_fullStr | Image Thresholding Improved by Global Optimization Methods |
| title_full_unstemmed | Image Thresholding Improved by Global Optimization Methods |
| title_short | Image Thresholding Improved by Global Optimization Methods |
| title_sort | image thresholding improved by global optimization methods |
| url | http://dx.doi.org/10.1080/08839514.2017.1300050 |
| work_keys_str_mv | AT felipebalabanian imagethresholdingimprovedbyglobaloptimizationmethods AT eduardosantanadasilva imagethresholdingimprovedbyglobaloptimizationmethods AT heliopedrini imagethresholdingimprovedbyglobaloptimizationmethods |