The basic theory and the key technologies are expounded , the main toll route are gray image target recognition 同时以灰度图像目标识别为主线，阐述与论文直接相关的基本理论和关键技术。
This thesis deals with gray image . the kernel of segmentation is pixel clustering . it belongs to optimization problem 本文处理的对象是灰度图像，分割的核心是对像素进行聚类，属于优化问题。
The mostly thought t are real time recognizing gray image target with gabor wavelet neural networks algorithm 本文的主要研究思想就是：运用并行的gabor小波神经网络算法对灰度图像目标进行实时识别。
The dissertation is the study of gabor wavelet neural network algorithm and its application in gray image target recognition 本文主要研究的是gabor小波神经网络算法及其在灰度图像目标识别中的应用。
In extracting texture characteristic , to cut down the amount of calculation , we compress the image and convert it to gray image 在提取纹理特征时，为了减少计算量，首先对原图像进行压缩和灰度化。
In a word , for color and gray images , this paper respectively proposed and realized an effective method to detect human faces 总之，针对彩色和灰度图像，本文分别提出和实现了一种有效的人脸检测方法。
A method of picking - up gray image edge on the basis of the mathematical morphology is presented and a simulation experiment was carried out 摘要提出了一种基于数学形态学的灰度图像边缘提取方法，并进行了仿真实验。
The multi channel gabor filter is designed based on theory and practicality , the texture features of gray image target are extracted 根据理论分析和实际需要，设计了多通道gabor滤波器，提取了灰度图像目标纹理特征。
Thirdly : during the course of gray image processing , we make gray image through filter , then give output of the filter wavelet transform 第四是小波软阈值的选择：本人把小波软阈值的选择跟图像信号的信噪比联系起来。
The original method just can be used in removing the noise from binary images . the gray images contain much information about writer habit 原算法只运用于对黑白二值图像去除噪声，而灰度图带有更多的反映书写人习惯的特征信息。
