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Saliency, scale and image description

机译:显着性,比例和图像描述

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摘要

Many computer vision problems can be considered to consist of two main tasks: the extraction of image content descriptions and their subsequent matching. The appropriate choice of type and level of description is of course task dependent, yet it is generally accepted that the low-level or so called early vision layers in the Human Visual System are context independent. This paper concentrates on the use of low-level approaches for solving computer vision problems and discusses three inter-related aspects of this: saliency, scale selection and content description. In contrast to many previous approaches which separate these tasks, we argue that these three aspects are intrinsically related. Based on this observation, a multiscale algorithm for the selection of salient regions of an image is introduced and its application to matching type problems such as tracking, object recognition and image retrieval is demonstrated.
机译:可以将许多计算机视觉问题视为包括两个主要任务:提取图像内容描述及其后续匹配。描述类型和描述级别的适当选择当然取决于任务,但是,人们普遍认为,人类视觉系统中的低层次或所谓的早期视觉层是上下文无关的。本文着重于使用低级方法来解决计算机视觉问题,并讨论了这三个相互关联的方面:显着性,量表选择和内容描述。与将这些任务分开的许多先前方法相反,我们认为这三个方面具有内在联系。基于这种观察,介绍了一种用于选择图像显着区域的多尺度算法,并说明了其在匹配类型问题(如跟踪,目标识别和图像检索)中的应用。

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