UTLN-Reflection

Existing datasets for reflection symmetry detection contain shapes which are single contour shapes, thus they are not really challenging. We also need to consider how well a symmetry detector works on complex/compound shapes where traditional methods based on contour approach can not. On the other hand, there is only one symmetrical axes for every shape of this dataset. Therefore, this fails to evaluate how a symmetry detector works on a shape containing several symmetrical axis and how good the detection is when the number of symmetrical axis is unknown. In order to address those above shortcomings, we introduce in this paper a new dataset, called ``UTLN Reflection dataset'', designed for evaluation of reflectional symmetry detection. The dataset, which is created by collecting free images on the Internet, contains two test suites: SRA (Single Reflection Axis) and MRA (Multiple Reflection Axes).

现有用于反射对称检测（reflection symmetry detection）的数据集仅包含单轮廓形状，因此检测任务缺乏足够挑战性。此外，现有数据集无法评估对称检测器在复杂/复合形状上的检测性能，而基于轮廓的传统方法对此类形状往往难以奏效。另一方面，现有数据集的每个形状仅包含一条对称轴，因此无法评估对称检测器在含多条对称轴的形状上的表现，以及当对称轴数量未知时的检测性能。为解决上述缺陷，本文提出了一款全新的「UTLN反射数据集（UTLN Reflection dataset）」，专为反射对称检测的性能评估所设计。该数据集通过收集互联网免费图片构建而成，包含两个测试集：单对称轴测试集（Single Reflection Axis，简称SRA）与多对称轴测试集（Multiple Reflection Axes，简称MRA）。