Ali, Ameer; Karmakar, Gour C. and Dooley, Laurence S.
PDF (Not Set)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
|Google Scholar:||Look up in Google Scholar|
Clustering algorithms are highly dependent on the features used and the type of the objects in a particular image. By considering object similar surface variations (SSV) as well as the arbitrariness of the fuzzy c-means (FCM) algorithm for pixel location, a fuzzy image segmentation considering object surface similarity (FSOS) algorithm was developed, but it was unable to segment objects having SSV satisfactorily. To improve the effectiveness of FSOS in segmenting objects with SSV, this paper introduces a new fuzzy image segmentation using suppressed fuzzy c-means clustering (FSSC) algorithm, which directly considers object SSV and incorporates the use of suppressed-FCM (SFCM) using pixel location. The algorithm also perceptually selects the threshold within the range of human visual perception. Both qualitative and quantitative results confirm the improved segmentation performance of FSSC compared with other algorithms including FSOS, FCM, possibilistic c-means (PCM) and SFCM for many different images.
|Item Type:||Conference Item|
|Academic Unit/Department:||Mathematics, Computing and Technology > Computing & Communications
Mathematics, Computing and Technology
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Laurence Dooley|
|Date Deposited:||15 Dec 2008 05:14|
|Last Modified:||25 Feb 2016 13:58|
|Share this page:|
► Automated document suggestions from open access sources
Download history for this item
These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.