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A cortical framework for invariant object categorization and recognition

机译：用于不变对象分类和识别的皮质框架

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摘要

In this paper we present a new model for invariant object categorization and recognition. It is based on explicit multi-scale features: lines, edges and keypoints are extracted from responses of simple, complex and end-stopped cells in cortical area V1, and keypoints are used to construct saliency maps for Focus-of-Attention. The model is a functional but dichotomous one, because keypoints are employed to model the "where" data stream, with dynamic routing of features from V1 to higher areas to obtain translation, rotation and size invariance, whereas lines and edges are employed in the "what" stream for object categorization and recognition. Furthermore, both the "where" and "what" pathways are dynamic in that information at coarse scales is employed first, after which information at progressively finer scales is added in order to refine the processes, i.e., both the dynamic feature routing and the categorization level. The construction of group and object templates, which are thought to be available in the prefrontal cortex with "what" and "where" components in PF46d and PF46v, is also illustrated. The model was tested in the framework of an integrated and biologically plausible architecture.
机译：在本文中，我们提出了一种用于不变对象分类和识别的新模型。它基于显式的多尺度特征：从皮质区域V1中的简单，复杂和末端停止的细胞的响应中提取线条，边缘和关键点，并将这些关键点用于构建关注焦点的显着图。该模型是一种功能强大但又一分为二的模型，因为关键点用于对“ where”数据流进行建模，并具有从V1到更高区域的要素动态路由，以获得平移，旋转和大小不变性，而线和边则用于“什么进行对象分类和识别。此外，“哪里”和“什么”途径都是动态的，因为首先采用了粗尺度的信息，然后添加了逐渐精细的尺度的信息，以完善过程，即动态特征路由和分类水平。还说明了组模板和对象模板的构造，这些模板被认为在前额叶皮层中具有PF46d和PF46v中的“ what”和“ where”组件。该模型在集成的，生物学上合理的架构的框架中进行了测试。

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