Name
Abstract | ||
|---|---|---|
n adaptive image multilevel thresholding segmentation algorithm is presented in this paper. The proposed algorithm introduces a parallel quantum genetic algorithm PQGA for histogram-based image segmentation. Quantum genetic algorithm QGA has the advantages of fast convergence speed and strong global search capabilities. And PQGA can improve the computational efficiency of the QGA further. Without predetermining the number of the thresholds, the proposed algorithm that chooses the automatic thresholding criterion as its objective function can obtain the number of the thresholds and the corresponding thresholds accurately. The experimental results demonstrate good performance of the PQGA in solving adaptive multilevel thresholding segmentation problems by comparing with other methods for several test images. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1504/IJCAT.2015.069334 | International Journal of Computer Applications in Technology |
Keywords | Field | DocType |
QGA, quantum genetic algorithm, multi-thresholding segmentation, image segmentation, PQGA, parallel quantum genetic algorithm, ATC, automatic thresholding criterion | Scale-space segmentation,Pattern recognition,Segmentation,Segmentation-based object categorization,Image processing,Image segmentation,Region growing,Artificial intelligence,Thresholding,Balanced histogram thresholding,Mathematics | Journal |
Volume | Issue | ISSN |
51 | 3 | 0952-8091 |
Citations | PageRank | References |
1 | 0.36 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Zhang | 1 | 6 | 4.96 |
| Huanzhou Li | 2 | 1 | 0.36 |
| Zhangguo Tang | 3 | 1 | 0.36 |
| Chang Liu | 4 | 1 | 0.69 |