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Topological methods for structural properties in artificial vision

机译:人工视觉中结构特性的拓扑方法

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摘要

The generation of symbolic representations from particular data provided by sensors is a difficult problem. To specify this generation we must extract features, perform their grouping, select models and display rules to match with abstract models represented as equivalence classes certain topological transformations. Thus, a reasonable strategy to obtain closed-form solutions would consist in the acquisition of rough models associated to equilibrium solutions in the static case; the application of perturbation techniques to initial systems and solutions, will allow us from low-level to high-level models. In the way, we must replace equilibrium by stable solutions.
机译:从传感器提供的特定数据生成符号表示是一个难题。要指定这一代,我们必须提取特征,执行特征分组,选择模型并显示规则,以与表示为等价类的某些拓扑转换的抽象模型相匹配。因此,获得封闭形式解的合理策略将包括在静态情况下获取与均衡解相关的粗糙模型。将扰动技术应用于初始系统和解决方案,将使我们从低级模型到高级模型。顺便说一下,我们必须用稳定的溶液代替平衡。

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