eprintid: 2610
rev_number: 7
eprint_status: archive
userid: 6
dir: disk0/00/00/26/10
datestamp: 2015-02-18 14:33:29
lastmod: 2015-02-18 14:33:29
status_changed: 2015-02-18 14:33:29
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 Survey of SOM-Based Active Contour Models for Image Segmentation
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
keywords: Image segmentation; Self Organizing Maps; active contours; SOM-based ACMs; topology preservation; neural networks
note: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014
abstract: Self Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly when dealing with image segmentation as a contour extraction problem. The idea of utilizing the prototypes (weights) of a SOM to model an evolving contour has produced a new class of Active Contour Models (ACMs), known as SOM-based ACMs. Such models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property, and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey paper, the main principles of SOMs and their application in modelling active contours are first highlighted. Then, we review existing SOM-based ACMs with a focus on their advantages and disadvantages in modelling the evolving contour via different kinds of SOMs. Finally, some current research directions are identified.
date: 2014
series: Advances in Intelligent Systems and Computing
number: 295
publisher: Springer
pagerange: 293-302
id_number: 10.1007/978-3-319-07695-9_28
refereed: TRUE
isbn: 978-3-319-07695-9
book_title: Advances in Self-Organizing Maps and Learning Vector Quantization
official_url: http://dx.doi.org/10.1007/978-3-319-07695-9_28
citation:   Abdelsamea, Mohammed and Gnecco, Giorgio and Gaber, Mohamed Medhat  A Survey of SOM-Based Active Contour Models for Image Segmentation.   In:  Advances in Self-Organizing Maps and Learning Vector Quantization.   Advances in Intelligent Systems and Computing  (295).  Springer, pp. 293-302.  ISBN 978-3-319-07695-9      (2014)