Perceptually grounded quantification of 2D shape complexity
Perceptually grounded quantification of 2D shape complexity
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DOI：
10.1007/s00371-022-02634-8
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Dena Bazazian;Bonnie Magland;C. Grimm;E. Chambers;Kathryn Leonard
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文献类型：
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作者：
Dena Bazazian;Bonnie Magland;C. Grimm;E. Chambers;Kathryn Leonard
测量形状复杂性的重要性可以从其广泛的应用中看出，如计算机视觉、机器人、认知研究、眼动追踪和心理学。然而，定义一个精确的度量来衡量形状的复杂性是非常具有挑战性的。在本文中，我们从数学、计算机科学和计算机视觉的既定工作中探索了形状复杂性的不同概念。我们将用户研究结果与定量分析相结合，从文献中先前考虑的近300种测量方法中，确定了捕获形状复杂性重要轴的三种测量方法。然后，我们探索具体措施和每种方法都能阐明的复杂性类型之间的联系。最后，我们提供了一个具有指定复杂性级别的抽象和有意义形状的数据集，以支持我们的发现并与其他研究人员分享。
The importance of measuring the complexity of shapes can be seen by the wide range of its application such as computer vision, robotics, cognitive studies, eye tracking, and psychology. However, it is very challenging to define an accurate and precise metric to measure the complexity of the shapes. In this paper, we explore different notions of shape complexity, drawing from established work in mathematics, computer science, and computer vision. We integrate results from user studies with quantitative analyses to identify three measures that capture important axes of shape complexity, out of a list of almost 300 measures previously considered in the literature. We then explore the connection between specific measures and the types of complexity that each one can elucidate. Finally, we contribute a dataset of both abstract and meaningful shapes with designated complexity levels both to support our findings and to share with other researchers.