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Variation models for simultaneous im...

Posirca, Iulia Magdalena.

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Variation models for simultaneous image segmentation and noise removal.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Variation models for simultaneous image segmentation and noise removal./
Author:	Posirca, Iulia Magdalena.
Description:	55 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
Contained By:	Dissertation Abstracts International74-09B(E).
Subject:	Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3569593
ISBN:	9781303069819

Variation models for simultaneous image segmentation and noise removal.
Posirca, Iulia Magdalena.

Variation models for simultaneous image segmentation and noise removal. - 55 p.

Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.

Thesis (Ph.D.)--University of Florida, 2012.

We present two projects for simultaneous image segmentation and noise removal. The first project concerns the images corrupted with Gaussian noise and the second one was developed for images contaminated with multiplicative noise. For both models we use soft segmentation, which allows each pixel to belong to each image pattern with some probability.

ISBN: 9781303069819Subjects--Topical Terms:

515831
Mathematics.

Variation models for simultaneous image segmentation and noise removal.
LDR:01795nam a2200289 4500 001 1967753
005 20141124124231.5
008 150210s2012 ||||||||||||||||| ||eng d
020 $a 9781303069819
035 $a (MiAaPQ)AAI3569593
035 $a AAI3569593
040 $a MiAaPQ $c MiAaPQ
100 1 $a Posirca, Iulia Magdalena. $3 2104821
245 1 0 $a Variation models for simultaneous image segmentation and noise removal.
300 $a 55 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
500 $a Adviser: Yunmei Chen.
502 $a Thesis (Ph.D.)--University of Florida, 2012.
520 $a We present two projects for simultaneous image segmentation and noise removal. The first project concerns the images corrupted with Gaussian noise and the second one was developed for images contaminated with multiplicative noise. For both models we use soft segmentation, which allows each pixel to belong to each image pattern with some probability.
520 $a Our work proposes also a functional with variable exponent, which provides a better noise removal with feature preserving. The diffusion resulting from the proposed models is a combination between the total variation (TV)-based and isotropic smoothing. To minimize the functional energy, we use the Euler-Lagrange equations on the (K-1)-simplex and the alternating minimization (AM) algorithm. The experimental and comparison results with some traditional models show the efficiency of our work, with improved denoising and segmentation of real and synthetic images.
590 $a School code: 0070.
650 4 $a Mathematics. $3 515831
650 4 $a Applied Mathematics. $3 1669109
690 $a 0405
690 $a 0364
710 2 $a University of Florida. $3 718949
773 0 $t Dissertation Abstracts International $g 74-09B(E).
790 $a 0070
791 $a Ph.D.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3569593