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On the Relationship between Variational Level Set-Based and SOM-Based Active Contours.

Abdelsamea MM, Gnecco G, Gaber MM, Elyan E - Comput Intell Neurosci (2015)

Bottom Line: Moreover, they can handle also topological changes of the contours.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Faculty of Science, University of Assiut, Assiut 71516, Egypt ; IMT Institute for Advanced Studies, Piazza S. Francesco 19, 55100 Lucca, Italy.

ABSTRACT
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.

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Related in: MedlinePlus

The architecture of the CSOM-CV ACM proposed in [102].
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Related In: Results  -  Collection


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fig5: The architecture of the CSOM-CV ACM proposed in [102].

Mentions: Figure 5 illustrates the off-line (i.e., training session) and on-line components of the CSOM-CV model. In the off-line session, the foreground supervised pixels are represented in light gray, while the background ones are represented in dark gray. The first SOM is trained using the intensity of the foreground supervised pixels, whereas the second one is trained using the intensity of the background supervised pixels. In such a session, the neurons of the two SOMs are arranged in such a way that the topological structure of the foreground and background intensity distributions are preserved. Finally, in the online session, the learned prototypes of the “foreground” and “background” neurons associated, respectively, with the two SOMs (and represented in light and dark gray, resp., in Figure 5) are used implicitly to control the evolution of the contour toward the true object boundary.


On the Relationship between Variational Level Set-Based and SOM-Based Active Contours.

Abdelsamea MM, Gnecco G, Gaber MM, Elyan E - Comput Intell Neurosci (2015)

The architecture of the CSOM-CV ACM proposed in [102].
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4417572&req=5

fig5: The architecture of the CSOM-CV ACM proposed in [102].
Mentions: Figure 5 illustrates the off-line (i.e., training session) and on-line components of the CSOM-CV model. In the off-line session, the foreground supervised pixels are represented in light gray, while the background ones are represented in dark gray. The first SOM is trained using the intensity of the foreground supervised pixels, whereas the second one is trained using the intensity of the background supervised pixels. In such a session, the neurons of the two SOMs are arranged in such a way that the topological structure of the foreground and background intensity distributions are preserved. Finally, in the online session, the learned prototypes of the “foreground” and “background” neurons associated, respectively, with the two SOMs (and represented in light and dark gray, resp., in Figure 5) are used implicitly to control the evolution of the contour toward the true object boundary.

Bottom Line: Moreover, they can handle also topological changes of the contours.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Faculty of Science, University of Assiut, Assiut 71516, Egypt ; IMT Institute for Advanced Studies, Piazza S. Francesco 19, 55100 Lucca, Italy.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus