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An extension of Marr's signature based edge classification and other methods determining diffuseness and height of edges, and bar edge width

机译：Marr基于签名的边缘分类的扩展，以及其他确定边缘的扩散度和高度以及条形边缘宽度的方法

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The authors classify edges with respect to diffuseness and profile type by using a tool evolved from scale space methodology called signatures. The method also enables the user to measure degree of diffuseness, height of edges, and bar edge width. The Gaussian difference signatures and apparent width signatures used are defined. To sift out the major edges, subsets of edge pixels must be selected. For describing the local gray level, landscape averaging needs to be done in a noncommittal fashion, as opposed to fitting a specific function blindly to data. Edge focusing, which is a coarse-to-fine traversal of brightness changes, deals with the subsets, whereas signatures may be thought of as a kind of reasonably cleverly done averaging. This method is of importance for distinguishing between illumination phenomena and object edges, performing depth-from-focus in single images, and in junction analysis.
机译：作者使用从比例空间方法演变而来的一种称为签名的工具，针对扩散和轮廓类型对边缘进行分类。该方法还使用户能够测量扩散程度，边缘高度和条形边缘宽度。定义了所使用的高斯差异签名和表观宽度签名。要筛选出主要边缘，必须选择边缘像素的子集。为了描述局部灰度，需要以非置信的方式进行风景平均，而不是盲目地将特定功能拟合到数据中。边缘聚焦是对亮度子集的粗略遍历，它处理这些子集，而签名可以认为是一种合理巧妙地完成的平均。该方法对于区分照明现象和物体边缘，在单个图像中执行聚焦深度以及在交界分析中非常重要。

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