发表机构
Ontario Tech University（安大略科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于笔的穿越手势技术viaCross，用于多对象和属性选择，通过笔画定义约束并配合小部件，实验表明其在复杂选择上更高效，但细化时需改进。
AI 中文摘要
基于穿越的选择已被广泛研究，但过去的研究忽视了使用穿越手势进行多对象和属性选择。我们提出了viaCross，一种基于笔的技术，其中穿越笔画定义属性约束，并与属性小部件配对，这些小部件可视化并允许后续编辑这些约束。一项对照研究将viaCross与传统WIMP过滤面板进行了比较。结果表明，viaCross在复杂的对象级选择上更高效，需要更少的笔画，并在任务难度增加时保持效率。WIMP面板在简单的基于属性的选择上更快且更直观，但参与者遇到了重复取消选择和偶尔的误点击。viaCross在创建属性选择方面与面板性能相当，但在细化过程中较慢，因为小部件必须重新创建，这表明需要持久小部件或与WIMP风格界面的混合支持。总体而言，viaCross证明了穿越手势可以为多对象和属性选择提供一种高效且可扩展的方法。
英文摘要
Crossing-based selection is well-studied, yet past research has overlooked multi-object and attribute selection with crossing gestures. We present viaCross, a pen-based technique in which crossing strokes define attribute constraints, paired with attribute widgets that visualize and allow subsequent edits to these constraints. A controlled study compared viaCross with traditional WIMP filter panels. Results show that viaCross was more efficient for complex object-level selections, requiring fewer strokes and maintaining efficiency as task difficulty increased. WIMP panels were faster and more intuitive for simple attribute-based selections, but participants encountered repeated deselections and occasional misclicks. viaCross matched panel performance for creating attribute selections but was slower during refinement, as widgets had to be recreated, indicating the need for persistent widgets or hybrid support with a WIMP-style interface. Overall, viaCross demonstrates that crossing-based gestures can provide an efficient and scalable approach to multi-object and attribute selection.
