Image Threshold and Segmentation
Image Threshold and Segmentation
复制标题
图像阈值和分割
DOI：
10.1002/0471443395.img040
复制
发表时间：
2002
影响因子：
1.3
通讯作者：
J. Russ
中科院分区：
文献类型：
--
作者：
J. Russ
将灰度图像或彩色图像还原为二值图像（黑白图像）的过程称为阈值分割或分割，其中的不同特征被隔离（通常用于测量）。这两个词指的是略有不同但又重叠的几套程序。阈值化通常意味着选择一个范围的灰度值或颜色值来区分特征和背景。它被广泛使用并且非常快速，因为它对图像进行全局操作，但通常不能提供完美的特征描述，因为一些像素（特别是沿着特征边缘）被错误分类，并且特征可能相互接触。分割意味着特征与背景以及彼此之间的分离，通常基于沿特征边界的局部像素值比较。这些方法通常很慢，可能无法找到所有存在的特征，有时还需要对特征的形状或大小进行假设。
The process of reducing a gray scale or color image to a binary (black and white) image in which distinct features are isolated (usually for measurement) is called thresholding or segmentation. The words refer to slightly different but overlapping sets of procedures. Thresholding usually implies the selection of a range of gray scale or color values that distinguish the features from the background. It is widely used and very fast since it operates globally on images, but often does not provide perfect delineation of features because some pixels (particularly along feature edges) are misclassified, and features may touch each other. Segmentation implies the separation of features from the background and each other, usually based on local comparison of pixel values along feature boundaries. These methods are often slow, may not find all of the features present, and sometimes require assumptions about the shape or size of the features.
Keywords:
thresholding;
segmentation;
gray level;
color images;
boundaries;
feature selection;
split-and-merge