Abdelsamea Mohammed M, Gnecco Giorgio, Gaber Mohamed Medhat, Elyan Eyad
Department of Mathematics, Faculty of Science, University of Assiut, Assiut 71516, Egypt ; IMT Institute for Advanced Studies, Piazza S. Francesco 19, 55100 Lucca, Italy.
IMT Institute for Advanced Studies, Piazza S. Francesco 19, 55100 Lucca, Italy.
Comput Intell Neurosci. 2015;2015:109029. doi: 10.1155/2015/109029. Epub 2015 Apr 19.
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.
大多数活动轮廓模型（ACM）将图像分割问题视为一个泛函优化问题，因为它们通过优化一个合适的泛函来将图像划分为多个区域。在ACM中，变分水平集方法已被用于构建活动轮廓，旨在对任意复杂形状进行建模。此外，它们还可以处理轮廓的拓扑变化。自组织映射（SOM）吸引了许多计算机视觉科学家的关注，特别是在基于利用SOM的原型（权重）来控制轮廓演化的思想对活动轮廓进行建模方面。一般来说，基于SOM的模型旨在利用SOM通过其拓扑保持特性学习边缘图信息的特定能力，并克服其他ACM的一些缺点，比如在这类模型中陷入要最小化的图像能量泛函的局部最小值。在本综述中，我们阐述基于变分水平集的ACM、基于SOM的ACM的主要概念及其关系，并从机器学习的角度全面回顾其最新模型的发展，重点关注它们的优缺点。
