ImgCropper

基于加权最小二乘最优化框架的滤波器。可用于实现图像的分层编辑。(Weighted least square)

程序代码说明 P0401：用Prewitt算子检测图像的边缘 P0402：用不同σ值的LoG算子检测图像的边缘 P0403：用Canny算子检测图像的边缘 P0404：图像的阈值分割 P0405：用水线阈值法分割图像 P0406：对矩阵进行四叉树分解 P0407：将图像分为文字和非文字的两个类别 P0408：形态学梯度检测二值图像的边缘 P0409：形态学实例——从PCB图像中删除所有电流线，仅保留芯片对象(Program code description P0401: Detects the edge of an image with the Prewitt operator P0402: LoG operator with different σ values is used to detect the edge of the image P0403: Detects the edge of an image with the Canny operator P0404: Threshold segmentation of the image P0405: Image segmentation using watermark threshold method P0406: Quadtree decomposition of the matrix P0407: The image is divided into text and non-text of the two categories P0408: Morphological gradient detects the edge of a binary image P0409: Morphology Example- Deletes all current lines the PCB image, leaving only the chip object)

图像分割，双峰法.显示直方图，根据直方图确定阈值，并根据次阈值进行图像的风格(segmation)

关于图像分割中分水岭的一个代码，能够实现相应功能，拿出来与大家共享(Image segmentation in the watershed on a code, can achieve the corresponding functions, and show to share with you)

双线性插值方法，输出像素的值为其坐标2x2领域内像素值的加权平均。(Bilinear interpolation method, the output pixel values for the coordinates of the area of 2x2 pixels of the weighted average value.)

对采样得到的180幅断层图像进行重建，并针对其中的一幅图像进行处理，并且统计图像中颗粒的大小和数目！(Tomographic images of 180 samples obtained were rebuilt, and for which an image processing and image size and number of particles statistics!)

TIP10的paper "Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization" 的代码。一个图像去噪和超分辨的算法(TIP10' s paper " Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization" code. Image denoising and super-resolution algorithm)

说明：  使用gpu、cpu并行进行sift算子计算匹配，能够在原来的基础上加速处理，但对显卡要求较高，具体环境配置使用方法可以参照mannual(SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussian pyramids and detect DoG Keypoints. Based on GPU list generation[3], SiftGPU then uses a GPU/CPU mixed method to efficiently build compact keypoint lists. Finally keypoints are processed parallely to get their orientations and descriptors. SiftGPU is inspired by Andrea Vedaldi s sift++[2] and Sudipta N Sinha et al s GPU-SIFT[4] . Many parameters of sift++ ( for example, number of octaves, number of DOG levels, edge threshold, etc) are also available in SiftGPU. The shader programs are dynamically generated according to the parameters that user specified. SiftGPU also includes a GPU exhaustive/guided sift matcher SiftMatchGPU. It basically multiplies the descriptor matrix on GPU and find closest feature matches on GPU. Both GLSL and CUDA implementations are provided. )

LBP（局部二值模式）特征提取，用于人脸识别，(LBP feature extraction for face recognition. . .)

图像处理领域最新的K-L变换后求主成分的程序。matlab文件。(image processing latest K-L transform PCA for the procedure. Matlab document.)