CS

c++编写的一个简单的类似Photoshop的图像编辑系统(c++ written in a simple system similar to the Photoshop image editing)

说明：  SIFT算法的区域匹配，比原有的SIFT+KD树匹配更加高效、准确。(different area SIFT algorithm which is better than the original SIFT and KD tree match.)

Retinex理论的基础理论是物体的颜色是由物体对长波（红色）、中波（绿色）、短波（蓝色）光线的反射能力来决定的，而不是由反射光强度的绝对值来决定的，物体的色彩不受光照非均匀性的影响，具有一致性，即retinex是以色感一致性（颜色恒常性）为基础的。不同于传统的线性、非线性的只能增强图像某一类特征的方法，Retinex可以在动态范围压缩、边缘增强和颜色恒常三个方面打到平衡，因此可以对各种不同类型的图像进行自适应的增强。(The basic theory of Retinex theory is the color of the object by object for the long wavelength (red), medium (green), short wave (blue) light reflection ability to decide, rather than by the intensity of reflected light of the absolute value to decide, the color of the object under illumination nonuniformity effects, consistent with the Retinex, that is based on color constancy (color constancy based). Different from the traditional linear, nonlinear image enhancement method of only one kind of features, Retinex can be in dynamic range compression, edge enhancement and three aspects of color constancy hit the balance, thus enhancing adaptive to a variety of different types of images can be.)

说明：  实现pca主成分分析算法的c语言代码，希望能帮到大家。(Pca principal component analysis algorithm to achieve the c language code, hope to help you.)

多光谱图像出理，离散傅立叶变换，低通滤波，TM条带影像去除(multispectral image integration of remote sensing)

双目立体匹配，使用SAD，SSD，NCC算法，比较多种常见算法的优劣，对其优化实现快速准确匹配。(Binocular stereo matching, the use of SAD, SSD, NCC algorithm, compare the merits of a variety of common algorithms, and its optimization to achieve fast and accurate matching)

VC计算机图形学原理及算法教程 VC计算机图形学入门教程(VC theory and algorithms of computer graphics computer graphics tutorial Tutorial VC)

一个有关边缘检测 robert sobel FFT傅里叶变换等算法的demo(a demo using VC++ that implements the algorithms of edge detection and FFT)

说明：  用c写的jpg编解码，包括jpeg2bmp和bmp2jpeg，可在vc6下调试。(With c written jpg codec, including jpeg2bmp and bmp2jpeg, may be under vc6 debugger.)

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.