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A Computational Model of the Short-Cut Rule for 2D Shape Decomposition

机译:二维形状分解捷径的计算模型

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We propose a new 2D shape decomposition method based on the short-cut rule. The short-cut rule originates from cognition research, and states that the human visual system prefers to partition an object into parts using the shortest possible cuts. We propose and implement a computational model for the short-cut rule and apply it to the problem of shape decomposition. The model we proposed generates a set of cut hypotheses passing through the points on the silhouette, which represent the negative minima of curvature. We then show that most part-cut hypotheses can be eliminated by analysis of local properties of each. Finally, the remaining hypotheses are evaluated in ascending length order, which guarantees that of any pair of conflicting cuts only the shortest will be accepted. We demonstrate that, compared with state-of-the-art shape decomposition methods, the proposed approach achieves decomposition results, which better correspond to human intuition as revealed in psychological experiments.
机译:我们提出了一种基于捷径法则的二维形状分解新方法。捷径规则源自认知研究,并指出人类视觉系统更喜欢使用尽可能短的捷径将对象划分为多个部分。我们提出并实现了一种捷径规则的计算模型,并将其应用于形状分解问题。我们提出的模型生成了一组通过轮廓上各点的割断假设,这些割断点代表负曲率最小值。然后,我们表明可以通过分析每个局部属性来消除大多数部分删减的假设。最后,剩余的假设按长度升序进行评估,这保证了任何一对冲突的切割都只会接受最短的切割。我们证明,与最新的形状分解方法相比,所提出的方法可实现分解结果,该分解结果更好地符合心理学实验中揭示的人类直觉。

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