Name
Title | ||
|---|---|---|
Infield oilseed rape images segmentation via improved unsupervised learning models combined with supreme color features |
Abstract | ||
|---|---|---|
•A new initialization scheme was proposed to optimize the performance of unsupervised learning algorithms.•Image segmentation was improved by incorporating supreme color features to the classification algorithm.•All clustering algorithms were proved to be robust under a complex field environment. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compag.2019.05.051 | Computers and Electronics in Agriculture |
Keywords | Field | DocType |
Image segmentation,Unsupervised learning,Genetic algorithm,Field phenotyping | Computer vision,Naive Bayes classifier,Segmentation,Image segmentation,Unsupervised learning,Artificial intelligence,Initialization,Engineering,Cluster analysis,Genetic algorithm,Mixture model | Journal |
Volume | ISSN | Citations |
162 | 0168-1699 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alwaseela Abdalla | 1 | 3 | 0.97 |
| Haiyan Cen | 2 | 4 | 5.39 |
| Ahmed El-manawy | 3 | 0 | 0.34 |
| Yong He | 4 | 44 | 15.57 |