Comparison of spatial relation definitions in computer vision
Comparison of spatial relation definitions in computer vision
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DOI：
10.1109/isuma.1995.527776
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发表时间：
1995-03
期刊：
影响因子：
--
通讯作者：
J. Keller;Xiaomei Wang
中科院分区：
文献类型：
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作者：
J. Keller;Xiaomei Wang
人类非常善于识别和标记视觉场景中的区域和对象。这种标记的线索之一是区域之间的空间关系。这通常与口译员对场景内容的理解和期望相结合。例如，通常情况下，在自然户外场景中，天空应该在树木上方，车辆应该在道路上。语境在图像的解读中起着非常重要的作用。这种空间关系的确定一直是一项难以自动化的任务。在定义数字图像中的区域之间的空间关系方面已经有几次尝试，最近，使用模糊集理论。在本文中，我们研究了三种定义空间关系的方法，以深入了解这种复杂的情况。
Humans are quite adept at recognizing and labeling regions and objects in visual scenes. One of the cues for such labeling is the spatial relationships exhibited among the regions. This is usually coupled with the interpreter's understanding and expectations of scene content. For example, it is normally the case that, in a natural outdoor scene, the sky should be above the trees and that vehicles should be on a road. Context plays a very important role in the interpretation of an image. This determination of spatial relations has been a difficult task to automate. There have been several attempts at defining spatial relationships between regions in a digital image, most recently, with the use of fuzzy set theory. In this paper, we examine three methods for defining spatial relations to gain insight into this complex situation.