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

Abstract EN

Most Active Contour Models (ACMs) deal with the image segmentationproblem as a functional optimization problem, as they work on dividingan image into several regions by optimizing a suitable functional.

Among ACMs,variational level set methods have been used to build an active contour with theaim of modeling arbitrarily complex shapes.

Moreover, they can handle also topologicalchanges of the contours.

Self-Organizing Maps (SOMs) have attractedthe attention of many computer vision scientists, particularly in modeling an activecontour based on the idea of utilizing the prototypes (weights) of a SOMto control the evolution of the contour.

SOM-based models have been proposedin general with the aim of exploiting the specific ability of SOMs to learn theedge-map information via their topology preservation property and overcomingsome drawbacks of other ACMs, such as trapping into local minima of the imageenergy functional to be minimized in such models.

In this survey, we illustratethe main concepts of variational level set-based ACMs, SOM-based ACMs, andtheir relationship and review in a comprehensive fashion the development of theirstate-of-the-art models from a machine learning perspective, with a focus on theirstrengths and weaknesses.