Image_Data1863613112005

本代码可以实现图像的维纳滤波，经过调试无错误，对于学习图像处理有较好帮助。(This code can achieve the image filtering, error-free through the debugging, better for learning image processing help.)

基于OPENCV的SIFT特征提取与匹配算法。包含完整的从图像高斯金字塔、DOG、空间极值点提取、关键点描述、KDtree匹配等关键步骤的全部函数实现，对全面深入理解Lowe的SIFT算法有莫大帮助。程序运行前须安装（1）OpenCV: http://opencvlibrary.sourceforge.net （2）SIFT: http://web.engr.oregonstate.edu/~hess/index.html，并配置其环境参数。(OPENCV the SIFT-based feature extraction and matching algorithm. Contains a complete Gaussian pyramid from the image, DOG, space extremum point extraction, description of key points, KDtree matching key step in the realization of the full function of the comprehensive and in-depth understanding of Lowe s SIFT algorithm of tremendous help. Program to run before the installation of (1) OpenCV: http://opencvlibrary.sourceforge.net (2) SIFT: http://web.engr.oregonstate.edu/ ~ hess/index.html, and configure the parameters of their environment.)

说明：  《visual C++ 开发经验技术宝典》图像锐化处理，主要是将图像的亮度增加，颜色的鲜艳感增强。("Visual C++ development experience Technical Collection," Image sharpening, mainly to increase the brightness of the image, bright sense of color enhancement. )

Some of image processing tools in visual C++

Choen-Sutherland算法裁剪线段 计算机图形学中使用Choen-Sutherland算法进行图形多边形裁剪，并用MFC实现(Choen-Sutherland )

数字图像处理英文版第三版60多M~很难找到！！希望大家珍惜再珍惜！(digital image processing English and third version,about over 60 M, difficult to find ,hope everybody cherish it !)

GDAL影像显示 建立金字塔通过i移动图像 进行图像的放大缩小 多波段显示影像金子塔显示 (GDAL image creates pyramid)

用大整数乘法来计算两个n位二进制数相乘(With large integer multiplication to calculate the multiplication of two n-bit binary number)

源码包括了常用的图像处理方法，代码详细，适合初学者(Source code, including the commonly used image processing method, the code in detail, suitable for beginners)

Gray Level Edge Thinning Method Abstract. An effective edge thinning algorithm is important in image segmentation and object identification since it increases the possibility of success in detecting objects in the image and saves the processing time in the further steps such as labeling and image transformation. We investigate some gray level edge thinning method that is based on the gradient magnitude within 3x3 local neighborhood. The new method has been tested with various types of images the real world. Experimental results show that this method produces more accurate images than those of the popular non-maximum suppression methods do.