缩放空间
素描
人工智能
代表(政治)
突出
计算机科学
模式识别(心理学)
直方图
光学(聚焦)
空格(标点符号)
比例(比率)
分割
图像处理
图像(数学)
计算机视觉
边缘检测
算法
物理
法学
光学
操作系统
政治
量子力学
政治学
摘要
This article presents: (i) a multiscale representation of grey-level shape called the scale-space primal sketch, which makes explicit both features in scale-space and the relations between structures at different scales, (ii) a methodology for extracting significant blob-like image structures from this representation, and (iii) applications to edge detection, histogram analysis, and junction classification demonstrating how the proposed method can be used for guiding later-stage visual processes. The representation gives a qualitative description of image structure, which allows for detection of stable scales and associated regions of interest in a solely bottom-up data-driven way. In other words, it generates coarse segmentation cues, and can hence be seen as preceding further processing, which can then be properly tuned. It is argued that once such information is available, many other processing tasks can become much simpler. Experiments on real imagery demonstrate that the proposed theory gives intuitive results.
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