Face-deformation

使用canny算子进行边缘检测的代码，效果很好(Using the canny edge detection operator code, good results)

基于规格化Hough变换的天波超视距雷达检测前跟踪算法(Hough transform based on the normalized horizon radar detection and tracking algorithms)

读取与显示JPG等格式图像文件，Visual C++编程技巧精选，很好的参考资料。(Read and display JPG format image files, Visual C++ programming skills selected, a good reference.)

完成对视频帧的四叉树分割，在纹理复杂处分割块较小，在纹理平坦处分割较大(Complete quadtree segmentation of video frames, complex area in texture segmentation smaller, flat larger regional segmentation block in texture)

实现图像中细胞的提取，并计算出细胞与核仁面积之比的可视化程序(Extraction of cells in the image, and calculate the cells with nucleolar area ratio of the visualization program)

图像简单处理小程序，带有测试图片，比较实用的小冬冬 (Simple procedures to deal with small images, with the test pictures, more practical small Dongdong)

毕业设计，基于内容的图像检索，支持的检索特征包括 sift,颜色直方图，灰度矩阵，HU不变矩，边缘方向直方图，检索方法使用K-means和K-D树两种，需要OPENCV支持，运行时请先选定一个文件夹来生成特征库，特征库用access数据库保存，只支持JPG文件(Graduate design, content-based image retrieval, search features, including support sift, color histogram, gray matrix, HU moment invariants, edge direction histogram, retrieval method using the K-means and KD trees are two kinds of needs OPENCV support Please select a runtime folder to generate the feature library, feature library with access database save, only supports JPG files)

Kalman+CamShift实现视频追踪(Kalman+CamShift video track)

卡尔曼滤波算法，opencv代码，鼠标跟踪(Kalman filter algorithm, opencv code, mouse tracking)

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.)