relation: http://eprints.imtlucca.it/2972/
title: A Concurrent SOM-Based Chan-Vese Model for Image Segmentation
creator: Abdelsamea, Mohammed
creator: Gnecco, Giorgio
creator: Gaber, Mohamed Medhat
subject: QA75 Electronic computers. Computer science
description: 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.
publisher: Springer
date: 2014
type: Book Section
type: PeerReviewed
identifier:   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)  
relation: http://dx.doi.org/10.1007/978-3-319-07695-9_19
relation: 10.1007/978-3-319-07695-9_19