Please use this identifier to cite or link to this item:
https://open.uns.ac.rs/handle/123456789/11810
Title: | Coverage segmentation based on linear unmixing and minimization of perimeter and boundary thickness | Authors: | Lindblad J. Sladoje N. |
Issue Date: | 15-Apr-2012 | Journal: | Pattern Recognition Letters | Abstract: | We present a method for coverage segmentation, where the, possibly partial, coverage of each image element by each of the image components is estimated. The method combines intensity information with spatial smoothness criteria. A model for linear unmixing of image intensities is enhanced by introducing two additional conditions: (i) minimization of object perimeter, leading to smooth object boundaries, and (ii) minimization of the thickness of the fuzzy object boundary, and to some extent overall image fuzziness, to respond to a natural assumption that imaged objects are crisp, and that fuzziness is mainly due to the imaging and digitization process. The segmentation is formulated as an optimization problem and solved by the Spectral Projected Gradient method. This fast, deterministic optimization method enables practical applicability of the proposed segmentation method. Evaluation on both synthetic and real images confirms very good performance of the algorithm. © 2011 Elsevier B.V. All rights reserved. | URI: | https://open.uns.ac.rs/handle/123456789/11810 | ISSN: | 01678655 | DOI: | 10.1016/j.patrec.2011.12.014 |
Appears in Collections: | FTN Publikacije/Publications |
Show full item record
SCOPUSTM
Citations
8
checked on May 10, 2024
Page view(s)
9
Last Week
7
7
Last month
0
0
checked on May 10, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.