Developing Perception-based Geometric Primitive-shape and Constraint Recognizers to Empower Instructors to Build Sketch Systems in the Classroom

本研究试图识别和测量感知和上下文特征对草图识别的影响，并使用这些结果创建有效的分类器来识别低级形状和约束，这些分类器将识别所有可能的解释，并为高级识别系统提供排序。要检查的上下文特征包括用户是否正在绘制或查看形状，用户是否正在查看美化或手绘的形状，伴随的手部运动，领域知识以及图中的伴随形状。用户感知研究将决定几何特征如何共变，以及形状应该如何变化以符合人类感知。图形图表是教育过程的重要组成部分。不幸的是，纠正这些问题很费时间，而且通常在测试过程中被忽略，尽管有证据表明测试有助于学习主题材料。草图识别系统可以用来识别手绘图，但目前它们需要很长时间来构建，并且需要草图识别方面的专业知识。这个项目有可能提供基础工作，从而导致开发一种工具，允许没有草图识别专业知识的教师构建他们自己的草图识别工具。此外，本项目提出构建基于感知和上下文的几何原语和约束识别器，通过更好地将基于计算机的识别与感知和上下文期望相匹配，使草图识别系统的创建对于非草图识别专家来说更加直观。该项目的成果将在LADDER/GUILD技术中实现，1)改善识别结果，使草图识别系统对指导员更有用；2)改进形状描述的自动生成，简化草图系统的创建，使其对指导员使用系统更实用。

This research attempts to identify and measure the effect of perceptual and contextual features on sketch recognition, and use these results to create effective classifiers to recognize low level shapes and constraints that will identify all possible interpretations along with a ranking for use by higher-level recognition systems. Contextual features to be examined include whether users are drawing or viewing a shape, whether users are viewing the beautified or hand-drawn shape, accompanying hand movements, domain knowledge, and accompanying shapes in the diagram. User studies in perception will determine how geometric features co-vary and how shapes should be varied to agree with human perception. Graphical diagrams are an important part of the educational process. Unfortunately, they are time-consuming to correct and are usually omitted from the testing process despite evidence that testing aids in learning of subject material. Sketch recognition systems can be built to recognize hand-drawn diagrams, but they currently take a long time to build and require expertise in sketch recognition. This project has the potential to provide foundational work that could lead to the development of a tool to allow instructors, without sketch recognition expertise, to build their own sketch recognition tools. Further, this project proposes to build geometric primitive and constraint recognizers based on perception and context to make the creation of sketch recognition systems more intuitive for non-experts in sketch recognition by better matching computer-based recognition to perceptual and contextual expectations. The results from this project will be implemented in the LADDER/GUILD technologies to 1) improve recognition results, making the sketch recognition systems more useful for instructors, and 2) improve automatic generation of shape descriptions to simplify sketch system creation, making it more practical for instructors to use the system.