Multiscale recursive medians, scale-space, and transforms with applications to image processing
Multiscale recursive medians, scale-space, and transforms with applications to image processing
复制标题
多尺度递归中值、尺度空间和变换及其在图像处理中的应用
DOI：
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发表时间：
1996
影响因子：
10.6
通讯作者：
R. Young
中科院分区：
文献类型：
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作者：
J. Bangham;Paul D. Ling;R. Young
级联递增尺度的一维递归中值滤波器产生一个筛子，称为 R 筛子，具有许多对图像处理很重要的属性。特别是，它 (1) 简化信号而不引入新的极值或边缘，也就是说，它保留了尺度空间。它与高斯滤波器共享此属性，但具有更加鲁棒的优点。 (2) 筛子的连续阶段之间的差异产生到粒度域的变换。可以使用幂等匹配筛在此域中识别图案和形状，并将结果转换回空间域。 R 筛的计算速度非常快，并且与具有平面结构元素的一维交替顺序滤波器有密切的关系。它们对于机器视觉应用很有用。
A cascade of increasing scale, 1-D, recursive median filters produces a sieve, termed an R-sieve, has a number of properties important to image processing. In particular, it (1) Simplifies signals without introducing new extrema or edges, that is, it preserves scale-space. It shares this property with Gaussian filters, but has the advantage of being significantly more robust. (2) The differences between successive stages of the sieve yield a transform, to the granularity domain. Patterns and shapes can be recognized in this domain using idempotent matched sieves and the result transformed back to the spatial domain. The R-sieve is very fast to compute and has a close relationship to 1-D alternating sequential filters with flat structuring elements. They are useful for machine vision applications.