relation: http://eprints.imtlucca.it/3125/
title: On the Relationship between Variational Level Set-Based and SOM-Based Active Contours
creator: Abdelsamea, Mohammed
creator: Gnecco, Giorgio
creator: Gaber, Mohamed Medhat
creator: Elyan, Eyad
subject: QA75 Electronic computers. Computer science
subject: RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
subject: T Technology (General)
description: Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based 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, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses.
publisher: Hindawi Publishing Corporation
date: 2015
type: Article
type: PeerReviewed
format: application/pdf
language: en
rights: cc_by_nc
identifier: http://eprints.imtlucca.it/3125/1/109029.pdf
identifier:   Abdelsamea, Mohammed and Gnecco, Giorgio and Gaber, Mohamed Medhat and Elyan, Eyad  On the Relationship between Variational Level Set-Based and SOM-Based Active Contours.  Computational Intelligence and Neuroscience, 2015 (109029).   ISSN 1687-5265      (2015)  
relation: http://dx.doi.org/10.1155/2015/109029
relation: 10.1155/2015/109029