Laboratory for Computational Vision, Department of Computer Science, University of British Columbia, Vancouver, B.C., V6T 1W5, Canada.
IEEE Trans Pattern Anal Mach Intell. 1986 Jan;8(1):34-43. doi: 10.1109/tpami.1986.4767750.
The problem of finding a description, at varying levels of detail, for planar curves and matching two such descriptions is posed and solved in this paper. A number of necessary criteria are imposed on any candidate solution method. Path-based Gaussian smoothing techniques are applied to the curve to find zeros of curvature at varying levels of detail. The result is the ``generalized scale space'' image of a planar curve which is invariant under rotation, uniform scaling and translation of the curve. These properties make the scale space image suitable for matching. The matching algorithm is a modification of the uniform cost algorithm and finds the lowest cost match of contours in the scale space images. It is argued that this is preferable to matching in a so-called stable scale of the curve because no such scale may exist for a given curve. This technique is applied to register a Landsat satellite image of the Strait of Georgia, B.C. (manually corrected for skew) to a map containing the shorelines of an overlapping area.
本文提出并解决了在不同详细程度下描述平面曲线并匹配两个此类描述的问题。对任何候选解决方案方法都施加了一些必要的标准。基于路径的高斯平滑技术应用于曲线，以在不同的详细程度下找到曲率的零点。结果是平面曲线的“广义尺度空间”图像，它在曲线的旋转、均匀缩放和平移下是不变的。这些特性使得尺度空间图像适合匹配。匹配算法是一致代价算法的修改，它在尺度空间图像中找到轮廓的最低代价匹配。有人认为，这比在曲线的所谓稳定尺度中进行匹配更好，因为对于给定的曲线，可能不存在这样的尺度。该技术应用于将不列颠哥伦比亚省乔治亚海峡的 Landsat 卫星图像（手动校正偏斜）注册到包含重叠区域海岸线的地图中。
