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GAMEs: growing and adaptive meshes for fully automatic shape modeling and analysis.

机译：游戏：不断增长的自适应网格，用于全自动形状建模和分析。

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This paper presents a new framework for shape modeling and analysis, rooted in the pattern recognition theory and based on artificial neural networks. Growing and adaptive meshes (GAMEs) are introduced: GAMEs combine the self-organizing networks which grow when require (SONGWR) algorithm and the Kohonen's self-organizing maps (SOMs) in order to build a mesh representation of a given shape and adapt it to instances of similar shapes. The modeling of a surface is seen as an unsupervised clustering problem, and tackled by using SONGWR (topology-learning phase). The point correspondence between point distribution models is granted by adapting the original model to other instances: the adaptation is seen as a classification task and performed accordingly to SOMs (topology-preserving phase). We thoroughly evaluated our method on challenging synthetic datasets, with different levels of noise and shape variations. Finally, we describe its application to the analysis of a challenging medical dataset. Our methodproved to be reproducible, robust to noise, and capable of capturing real variations within and between groups of shapes.
机译：本文提出了一种基于模式识别理论并基于人工神经网络的形状建模和分析新框架。介绍了不断增长的自适应网格（GAME）：GAME将按需增长的自组织网络（SONGWR）算法和Kohonen的自组织图（SOM）结合在一起，以构建给定形状的网格表示并使其适应形状相似的实例。表面的建模被视为无监督的聚类问题，可以通过使用SONGWR（拓扑学习阶段）来解决。点分布模型之间的点对应关系是通过使原始模型适应其他情况而授予的：该适应被视为分类任务，并根据SOM进行相应的操作（拓扑保存阶段）。我们在具有不同水平的噪声和形状变化的具有挑战性的合成数据集上彻底评估了我们的方法。最后，我们描述了其在具有挑战性的医学数据集分析中的应用。我们的方法被证明具有可重现性，抗噪性，并且能够捕获形状组内和形状组之间的真实变化。

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