MATLAB-image-processing-(can-see)

用于对图像特征的分类，随机森林算法是一类很好的算法(For the classification of image features, random forest algorithm is a kind of a good algorithm)

针对 Anrlod 变换的周期依赖于图像的阶数这一特性, 提出了一种反变换算法 。 该算法 通过分析加密图像任一点处两坐标分量间关系, 得到原图像相应点的坐标 , 从而实现图像的解密。(In view of the characteristic that the Anrlod transform cycle depends on the order of the image, an inverse transformation algorithm is proposed. The algorithm obtains the coordinates of the corresponding points of the original image by analyzing the relationship between the two coordinate components at any point of the encrypted image, so as to achieve the decryption of the image.)

C# 图片推拉效果 实例

strtok(char s[], const char *delim) 用来将字符串分割成一个个片段。参数s指向欲分割的字符串，参数delim则为分割字符串中包含的所有字符。当strtok()在参数s的字符串中发现参数delim中包涵的分割字符时,则会将该字符改为

可以实现平面图像到球面坐标的映射变换，效果较好，附原始图片。(Plane image mapping transformation to spherical coordinates better with the original picture.)

基于小波多尺度的灰度图像纹理特征检测源码，小波可以自定义(Based on wavelet multi-scale texture features of the gray image source detection, wavelet can customize)

tiff格式的lena原图，该图像包含了各种细节、平滑区域、阴影和纹理，这些对测试各种图像处理算法很有用。(Lena artwork in TIFF format,The image contains a variety of detail, smooth areas, shadows, and textures, which are useful for testing various image processing algorithms.)

对两帧图像进行ICM调试.包含内容：1估计姿态和摄像机内参数；2两幅图做四次金字塔；3利用输入特征点采用ransac算法估计姿态R, T。4sift特征匹配；5摄像机标定。(ICM images on two debugging. Contains: 1 Estimated posture and intrinsic parameters 2 two plans to do four pyramids 3 using the input feature points using ransac algorithm estimates gesture R, T. 4sift feature matching 5 camera calibration.)

对测井曲线进行重构的程序，可以充分利用测井特征曲线所反应的岩性特征信息，完成多信息的融合(Log curve reconstruction procedures)

压缩感知中利用增广拉格朗日方程解最小稀疏正则化的恢复算法(DAL solves the dual problem of (1) via the augmented Lagrangian method (see Bertsekas 82). It uses the analytic expression (and its derivatives) of the following soft-thresholding operation， which can be computed for L1 and grouped L1 (and many other) sparsity inducing regularizers. If you are interested in our algorithm please find more details in our technical report or in my talk at Optimization for Machine Learning Workshop (NIPS 2009).)