otsu-and-interation

图像纹理特征提取，是图像处理中一另一个非常重要的底层特征，matlab实现(Image texture feature extraction, image processing are very important to one another underlying characteristics, matlab implementation)

基于上下文注意的显著区域检测，附件包括代码和英文文章。(Context-Aware Saliency Detection. The file includes the paper and the source code in matlab.)

snake活动轮廓边界提取源码.不错的算法哦(active contour snake boundary extraction source. Oh good algorithm)

多个图像增强的源代码 包括小波增强 边缘增强 直方图滤波等等。。。(Multiple image enhancement of source code includes wavelet edge histogram filtering, enhance enhancement.)

指纹识别系统，包括指纹图像预处理，特征点提取，采用点模式匹配。(Fingerprint identification systems, including fingerprint image preprocessing, feature extraction, using point pattern matching.)

这个函数分配一个数据缓冲区与多个颜色指定系统上的乐队。这种类型的缓冲区允许表示彩色图像(例如,RGB)。 这个函数为每个指定分配缓冲区,一个二维表面颜色乐队。您可以使用MbufAlloc1d()和MbufAlloc2d()来创建单独的乐队一或二维 数据缓冲区,分别。 分配一个缓冲区后,我们建议您检查是否操作成功,使用MappGetError(),或通过验证,不是M_NULL返回的缓冲区的标识符。(This function allocates a data buffer with multiple color bands on the specified system. This type of buffer allows for the representation of color images (for example, RGB). This function allocates buffers that have a two-dimensional surface for each specified color band. You can use MbufAlloc1d() and MbufAlloc2d() to create single band one- or two-dimensional data buffers, respectively. After allocating a buffer, we recommend that you check if the operation was successful, using MappGetError(), or by verifying that the buffer identifier returned is not M_NULL.)

说明：  toolbox_calib图像畸变矫正工具箱(Image distortion correction toolbox)

二值化图像,提取图像中的圆形，例如提取照片中的车轮(Extracting a circular image, for example, extraction wheel photo)

matlab实现的小波降噪声图像处理算法，效果十分好，自己做了调试。(The wavelet reduced noise image processing algorithm realized by Matlab is very effective and has been debugged.)

EDLines是一种快速直线检测算子，2012年在ICCV上提出，也是目前处理直线检测最快的算法之一，该算法包含三个步骤：（1）边缘提取：利用Edge Drawing (ED)算法[28,29]从灰度图像中提取边缘片段；（2）线段检测：利用最小二乘法提取直线段；（3）线段确认：遵循Helmholtz定律，从已提取的直线段中摒弃虚假线段。EDLines算法的优越性得益于Edge Drawing (ED)算法能够从灰度图像中准确、快速、稳定地提取出光滑、完整的边缘片段。Edge Drawing (ED)算法包含以下步骤： （1）采用size=5*5,σ=1的高斯核对灰度图像进行平滑滤波，去除噪声； （2）采用一种常用的梯度算子，如Prewitt、Sobel或Scharr等计算平滑后图像中每个像素点的梯度幅度和梯度方向； （3）将梯度图中邻域内幅度最大的像素点标记为锚点，这些锚点成为图像边缘点(edge elements)的概率很大； （4）将相邻的锚点连接成边缘线。从一个锚点起始，ED利用相邻像素的梯度幅度和方向在梯度为最大值的锚点之间游走。 (Edge Drawing (ED) is our recently-proposed, novel, fast edge detection algorithm. What makes ED stand out the existing edge detectors, e.g., Canny, is the following: While the other edge detectors give out a binary edge image as output, where the etected edge pixels are usually independent, discontinuous entities ED produces a set of edge segments, which are clean, contiguous, i.e., connected, chains of edge pixels. Thus, while the output of the other edge detectors requires urther processing to generate potential object boundaries, which may not even be possible or result in inaccuracies ED not only produces perfectly connected object boundaries by default, but it also achieves this in blazing speed compared to other edge detector.)