The week's pick
Reseach Article
Improved Felicm based Underwater Color Image Segmentation by using L0 Gradient Minimization and DBPTGMF
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 105 - Number 16 |
| Year of Publication: 2014 |
| Authors: Rozy Kumari, Narinder Sharma |
10.5120/18463-9826
|
Rozy Kumari, Narinder Sharma . Improved Felicm based Underwater Color Image Segmentation by using L0 Gradient Minimization and DBPTGMF. International Journal of Computer Applications. 105, 16 ( November 2014), 32-37. DOI=10.5120/18463-9826
Abstract
Image segmentation is the method of dividing a digital image into several segments. The aim of segmentation is to simplify or modify the signification of an image into meaningful form that is more significant and easier to examine. It is generally used to put objects and edges in images. Various methods of image segmentation are thresholding, compression-based, histogram based etc. From the survey it has been concluded that none of the method has been very much efficient for segmentation in various types of images. So, to overcome this issue, a new method of segmentation has been proposed. New hybrid image segmentation by using FELICM, L_0 gradient minimization and the Decision based partial trimmed global mean filter has been proposed in this paper. To evaluate the effectiveness of the proposed technique on different kinds of images, various performance metrics have been considered. The method has shown much effective results for underwater and natural images.
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