fcm_fcm

基于样图的纹理合成 效果很好的块匹配合成(Based Texture Synthesis from Samples effective synthesis of block-matching)

基于双边滤波的retinex图像增强算法(Retinex Algorithm for Image Enhancement Based on Bilateral Filtering)

Matlab 实现的带阻滤波器设计。基于高斯带阻滤波器。实现冈萨雷斯《数字图像处理》第五章的实例。(Matlab implementation of the band stop filter design. Based on Gaussian band-stop filter. Achieve Gonzalez, " Digital Image Processing" Chapter V of the instance.)

snake模型寻找图像边界，以及改进的GVF算法(snake model for image border, and to improve the algorithm GVF)

very useful toolbox for the matlab user

可以Raw格式图片(you can open raw file with the .m code.)

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.)

说明：  ① 打开图片文件； ② 实现图像的放大、缩小功能； ③ 实现图像的黑白处理。 (black setting open file)

阴影检测,基于OpenCV用于对图像中阴影的检测(Shadow detection, OpenCV for image shadow detection)

将一幅图像划分成8*8图像块，计算灰度梯度共生矩阵，基于混合熵对每个图像块分类(divide an image into 8*8 image blocks,calculate gray-gradient-matrix, classify image blocks based on the mixed entropy)