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.
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Abdelsamea, M.M., Gnecco, G., Gaber, M.M. (2014). A Survey of SOM-Based Active Contour Models for Image Segmentation. In: Villmann, T., Schleif, FM., Kaden, M., Lange, M. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-07695-9_28
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DOI: https://doi.org/10.1007/978-3-319-07695-9_28
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