SGER: Intermediate-level Structural Categories from Visual Complexity Analysis

该奖项资助视觉图像结构理论基础的初步探索，以帮助支持对图像档案的查询。 数字图像档案正在激增，这就提出了如何从中检索特定图像的问题。 然而，在这方面， 虽然查询自然是以高级功能术语提出的，但是这种查询的实现是基于低级图像运算符的。视觉结构的中级理论是必要的，以弥补这一差距，这正是本研究所解决的问题。 基于边缘元素作为切线估计的解释，它是从几何测度和复杂性理论推导出来的。 从下面看，该理论提供了 一个 根据边缘元素的组织 尺寸。 它们要么被绑定到扩展的（1维）组中，要么被绑定到抽象不同的（2维）结构类中， 纹理类型或（0维）方向不连续性。 从上面看，表示法诱导结构的等价类，例如边界轮廓，头发和草图案的纹理流，以及遮挡点的“T”形连接。因此，可以将涉及森林的查询与涉及毛发图案或湍流的查询区分开来，并且可以将多个重叠的对象分成组件。 此外，图像和绘图可以从文本页面中分割出来。

This award funds an initial exploration of a theoretical basis for visual image structure, to aid in supporting queries to image archives. Digital image archives are proliferating, which raises the question of how to retrieve specific images from them. However, while queries are naturally posed in high-level, functional terms, the realizations of such queries are based on low-level image operators. An intermediate-level theory of visual structure is necessary to close this gap, which is precisely what this research addresses. Based on an interpretation of edge elements as tangent estimates, it is derived from geometric measure and complexity theory. Viewed from below, the theory provides an organization for edge elements according to dimension. They are either bound into extended (1-dimensional) groups, into (2-dimensional) structural classes that abstract different types of texture, or (0-dimensional) orientation discontinuities. Viewed from above, the representation induces equivalence classes of structure, such as bounding contours, texture flows for hair and grass patterns, and ``T''-junctions for points of occlusion. Thus queries involving forests can be differentiated from those involving fur patterns or turbulent water, and multiple, overlapping objects can be separated into components. Furthermore, images and drawings can be segmented from pages of text.