One of the purposes of perception is to bridge between sensors and conceptual understanding. Marr's Primal Sketch combined initial edge-finding with multiple downstream processes to capture aspects of visual perception such as grouping and stereopsis. Given the progress made in multiple areas of AI since then, we have developed a new framework inspired by Marr's work, the Hybrid Primal Sketch, which combines computer vision components into an ensemble to produce sketch-like entities which are then further processed by CogSketch, our model of high-level human vision, to produce both more detailed shape representations and scene representations which can be used for data-efficient learning via analogical generalization. This paper describes our theoretical framework, summarizes several previous experiments, and outlines a new experiment in progress on diagram understanding.

感知的一个目的是在传感器和概念理解之间搭建桥梁。本文提出了一个受马尔（Marr）工作启发的新框架，即混合原始素描（Hybrid Primal Sketch），它将计算机视觉组件结合为一个集合，通过CogSketch模型进一步处理生成类似素描的实体，从而产生更详细的形状表示和场景表示，可用于通过类比推理实现数据高效学习。本文描述了我们的理论框架，总结了几个先前的实验，并概述了正在进行的一个关于图解理解的新实验。

混合原始素描：结合类比、定性表征和计算机视觉的场景理解