eprintid: 2972
rev_number: 6
eprint_status: archive
userid: 69
dir: disk0/00/00/29/72
datestamp: 2015-12-15 11:27:37
lastmod: 2016-03-18 10:39:28
status_changed: 2015-12-15 11:27:37
type: book_section
metadata_visibility: show
creators_name: Abdelsamea, Mohammed
creators_name: Gnecco, Giorgio
creators_name: Gaber, Mohamed Medhat
creators_id: mohammed.abdelsamea@imtlucca.it
creators_id: giorgio.gnecco@imtlucca.it
creators_id: 
title: A Concurrent SOM-Based Chan-Vese Model for Image Segmentation
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
keywords: Image segmentation; Chan-Vese model; Concurrent Self Organizing Maps; global active contours; neural networks
abstract: Concurrent Self Organizing Maps (CSOMs) deal with the pattern classification problem in a parallel processing way, aiming to minimize a suitable objective function. Similarly, Active Contour Models (ACMs) (e.g., the Chan-Vese (CV) model) deal with the image segmentation problem as an optimization problem by minimizing a suitable energy functional. The effectiveness of ACMs is a real challenge in many computer vision applications. In this paper, we propose a novel regional ACM, which relies on a CSOM to approximate the foreground and background image intensity distributions in a supervised way, and to drive the active-contour evolution accordingly. We term our model Concurrent Self Organizing Map-based Chan-Vese (CSOM-CV) model. Its main idea is to concurrently integrate the global information extracted by a CSOM from a few supervised pixels into the level-set framework of the CV model to build an effective ACM. Experimental results show the effectiveness of CSOM-CV in segmenting synthetic and real images, when compared with the stand-alone CV and CSOM models.
date: 2014
date_type: published
series: Advances in Intelligent Systems and Computing
volume: 295
publisher: Springer
pagerange: 199-208
id_number: 10.1007/978-3-319-07695-9_19
refereed: TRUE
isbn: 978-3-319-07694-2
book_title: Advances in Self-Organizing Maps and Learning Vector Quantization
official_url: http://dx.doi.org/10.1007/978-3-319-07695-9_19
citation:   Abdelsamea, Mohammed and Gnecco, Giorgio and Gaber, Mohamed Medhat  A Concurrent SOM-Based Chan-Vese Model for Image Segmentation.   In:  Advances in Self-Organizing Maps and Learning Vector Quantization.   Advances in Intelligent Systems and Computing, 295 .  Springer, pp. 199-208.  ISBN 978-3-319-07694-2      (2014)