| dc.contributor.author |
Tambouratzis, G |
en |
| dc.date.accessioned |
2014-06-06T06:43:24Z |
|
| dc.date.available |
2014-06-06T06:43:24Z |
|
| dc.date.issued |
1997 |
en |
| dc.identifier.issn |
09410643 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/1237 |
|
| dc.relation.uri |
http://www.scopus.com/inward/record.url?eid=2-s2.0-0031500535&partnerID=40&md5=810eeff342d3000f2dc17f809a9e8adf |
en |
| dc.subject |
Analysis of pattern space characteristics |
en |
| dc.subject |
Logic neural networks |
en |
| dc.subject |
Self-organisation |
en |
| dc.subject |
Texture-based image segmentation |
en |
| dc.subject |
Variable sensitivity |
en |
| dc.title |
Image segmentation with the SOLNN unsupervised logic neural network |
en |
| heal.type |
journalArticle |
en |
| heal.publicationDate |
1997 |
en |
| heal.abstract |
In this article, an image segmentation method based on the SOLNN self-organising logic neural network is studied. The input image is initially processed using the TCS texture-highlighting technique and is then presented to the SOLNN network which segments it. The SOLNN is characterised by a variable sensitivity which enables it to be fine-tuned to detect different sub-textures within each texture to the desired degree of detail. The experimental results reported here illustrate the fact that the SOLNN indeed clusters accurately the textural information so that each cluster represents a single texture even for images which are objectively very difficult to segment. Thus, it is supported that the proposed approach leads to the design of an effective texture-based image-segmentation system. |
en |
| heal.journalName |
Neural Computing and Applications |
en |
| dc.identifier.issue |
2 |
en |
| dc.identifier.volume |
6 |
en |
| dc.identifier.spage |
91 |
en |
| dc.identifier.epage |
101 |
en |