Shape complexity based on mutual information
Shape complexity based on mutual information
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基于互信息的形状复杂性
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
M. Sbert
中科院分区：
文献类型：
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作者：
Jaume Rigau;M. Feixas;M. Sbert
形状复杂性最近受到了不同领域的关注，例如计算机视觉和心理学。本文运用积分几何和信息论工具，从两个不同的角度来量化形状复杂性：从物体内部，我们评估其结构程度或其表面之间的关联程度（内部复杂性）；从外部，我们计算其与外围球体的相互作用程度（外部复杂性）。我们的形状复杂性度量基于以下两个事实：均匀分布的全局线穿过物体定义了一个连续的信息通道，该通道的连续互信息独立于物体的离散化，并且不受平移、旋转和尺度变化的影响。本文介绍的方法可以潜在地用作物体识别、图像检索、物体定位、肿瘤分析和蛋白质对接等的形状描述符。
Shape complexity has recently received attention from different fields, such as computer vision and psychology. In this paper, integral geometry and information theory tools are applied to quantify the shape complexity from two different perspectives: from the inside of the object, we evaluate its degree of structure or correlation between its surfaces (inner complexity), and from the outside, we compute its degree of interaction with the circumscribing sphere (outer complexity). Our shape complexity measures are based on the following two facts: uniformly distributed global lines crossing an object define a continuous information channel and the continuous mutual information of this channel is independent of the object discretisation and invariant to translations, rotations, and changes of scale. The measures introduced in this paper can be potentially used as shape descriptors for object recognition, image retrieval, object localisation, tumour analysis, and protein docking, among others.