形态学图像增强算法，包括对比度增强和边缘锐化

Simple face and eye detection MATLAB Release: R13 You can use this codes for face detection based on color segmentation and eye region detection.

JPEG2000标准的实现软件Kakadu的近期版本5.2.5的可执行程序，支持多核解码编码，cpu的性能可以得到充分发挥，是JPEG2000软件中已知性能最高的-JPEG2000 standard software Kakadu recent version 5.2.5 of the executable program to support multi-core codec encoding, cpu

texture transfer example

该文献基于多层理想滤波器的纹理特征提取方法,对做纹理图像特征提取的帮助很大。-filter,wenlitezheng ,image

通过LevelSet算法以图像进行分割算法 ,-Through LevelSet algorithm to image segmentation algorithm,

针对摄像机固定和共面约束条件下进行目标定位,提出了一种简单可行的全新算法。该算法避开了烦琐的摄像机标定这一过程,也不用精确地求解单应性矩阵。只是运用射影几何的性质,通过简单的几何关系,对非标定单摄像机拍摄的单幅图像,求解其从2D图像平面到真实的3D平面的非线性单映射关系。该算法为解决计算机视觉的测量、定位、三维重建等问题,提供一种全新的思路。 -against cameras fixed and coplanar binding conditions for target location, a simple new algorithm. The algorithm avoid the cumbersome process of the camera calibration, and it does not accurately be solved single matrix. Just use projective geometry of nature, by simple geometry, orders for non-standard video camera in a single image. Solving its two D images from two-dimensional to three real plane D nonlinear single map. The algorithm to solve the measurement of computer vision, positioning, three-dimensional reconstruction, with a brand new idea.

边缘检测Sobel算子实现数字图像处理中边缘检测-Sobel edge detection operator of digital image processing edge detection

图像的形态学变换（腐蚀运算、膨胀运算、细化等）-Morphological image transform (corrosion computing, expanding operations, refining, etc.)

用C++实现的OTSU大津阈值分割方法，使用的是图像的全局阈值-With C++ Realize the OTSU Otsu threshold segmentation method, using images of the overall threshold

30个图像处理的VC++ 源代码，包括256色转灰度图,Hough变换,image_j1,PCA,Walsh变换,对比度拉伸,二值化变换,反色, 方块编码,傅立叶变换,高斯平滑,灰度均衡,均值滤波,拉普拉斯锐化（边缘检测）, 离散余弦变换,亮度增减,逆滤波处理,取对数,取指数,梯度锐化,图象处理,图像镜像, 图像平移,图像缩放,图像细化,图像旋转,维纳滤波处理,用Canny算子提取边缘,阈值变换,直方图均衡.-30 image processing VC++ Source code, including 256 colors to grayscale, Hough transform, image_j1, PCA, Walsh transform, contrast stretching, binarization transformation, anti-color, box encoding, Fourier Transform , Gaussian smoothing, gray balance, mean filter, Laplacian sharpening (edge detection), discrete cosine transform, the brightness increases and decreases in inverse filtering deal, check on the number of check index, the gradient sharpening, image processing, mirror image, image translation, image scaling, image thinning, image rotation, Wiener filtering treatment using Canny edge extraction operator, threshold transform, histogram equalization.