Name
Abstract | ||
|---|---|---|
This article presents a new form of indiscernibility relation based on K-means clustering of pixel values. The end result is a partitioning of a set of pixel values into bins that represent equivalence classes. The proposed approach makes it possible to introduce a form of upper and lower approximation specialized relative to sets of pixel values. This approach is particularly relevant to a special class of digital images for power line ceramic insulators. Until now the problem of determining when a ceramic insulator needs to be replaced has relied on visual inspection. With the K-means indiscernibility relation, it is now possible to automate the detection of faulty ceramic insulators. The contribution of this article is the introduction of an approach to classifying power line insulators based on a rough set methods and K-means clustering in analyzing digital images. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-25929-9_71 | LECTURE NOTES IN ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
approximation,classification,digital image processing,K-means clustering,rough sets | k-means clustering,Electrical network,Computer science,Algorithm,Digital image,Rough set,Pixel,Equivalence class,Cluster analysis,Digital image processing | Conference |
Volume | ISSN | Citations |
3066 | 0302-9743 | 9 |
PageRank | References | Authors |
0.50 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James F. Peters | 1 | 1825 | 184.11 |
| Maciej Borkowski | 2 | 69 | 10.29 |