实验结果表明该算法克服了光照不均和噪声对灰度文本图像二值化的影响，得到了较理想的二值图像。
The experiment results show that with the algorithm, the influence of uneven light and noise on the binarization of gray character images has been overcome and an excellent binary image is obtained.
本文重点研究了数学形态学在二值细胞图象和灰度细胞图象分割处理中的应用。
In this paper, the emphasis is the segmentation application of mathematical morphology to binary cell's image and gray-scale cell's image.
指纹图像的预处理又可以分为灰度图滤波去噪、二值化、二值化图像去噪、细化和细化后去噪五个部分。
Fingerprint image pre-processing has five parts: filtration in gray-scale image, binarization, filtration in binary image, thinning and filtration in thinning image.
通过灰度拉伸增强图像对比度，通过二值化处理实现图像中背景和对象的分割。
The contrast of the image was enhanced by gray expanding and the key threshold of the binarization algorithm was determined based on the dynamical threshold method.
整个过程基本是对灰度图像和二值模板的形态处理，简单易行。
The whole process is the morphology processing based on the gray-level image and binary mask, and is simple and easy to implement.
同otsu算法和灰度平均值算法比较，该图像二值化方法在具有小区域缺陷特征图像处理方面有一定的优势。
Compared with the Mean Gray Level and OTSU algorithm, the binary conversion method has advantages of processing small-area defect images.
本文介绍了几种主要的预处理方法，如几何归一化，灰度归一化和图像二值化。
In this paper, several key preprocessing methods are introduced, such as geometry normalization, gray-scale normalization and images binary-conversion.
首先在二值图像上去掉头发，利用灰度投影和区域生长的方法分别粗定位和精确定位眼睛位置。
Firstly, we should remove hair in binary image, and then locate eyes' position with the method of gray projection and area growth.
首先对灰度图进行二值化处理，然后利用数学形态学的基本运算——膨胀运算和闭运算对二值图进行消噪及去毛刺处理。
Firstly, gray image changed into binary image. Then, noise and stub burr is removed by using basic operation of mathematical morphology-dilation and closing operation.
并通过灰度图像互相关和二值化图像互相关的实验对比与分析，得知二值化图像互相关的灵敏度比灰度图像互相关的灵敏度要高。
The experiment results of the method of characteristics and those of the correlation fitted well. The correlations of gray patterns or of binary patterns were done respectively.
因此该方法除能用于任意灰度起点的一般意义上的二值化外，特别适宜于图像的挖掘和隐藏。
So it was fit for the image mining or image hiding besides it can be used for the common bi-valuation of random gray of starting point.
因此该方法除能用于任意灰度起点的一般意义上的二值化外，特别适宜于图像的挖掘和隐藏。
So it was fit for the image mining or image hiding besides it can be used for the common bi-valuation of random gray of starting point.
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