LBP

说明：  应用条件随机场进行图像分割 使用方法：将pydensecrf-py27或pydensecrf-py35重命名为pydensecrf，放入C:Program FilesAnaconda3Libsite-packages 运行：python inference.py im1.png anno1.png out.png(Renameing the file "pydensecrf-py27" or "pydensecrf-py35" to the new name "pydensecrf" Puting this file "pydensecrf" in this directory "C:Program FilesAnaconda3Libsite-packages" cd examples/ python inference.py im1.png anno1.png out.png)

说明：  图像处理案例，本程序是用matlab基于形态学的权重完成自适应图像去噪功能(Image processing case, this program is based on the weight of MATLAB morphology to complete adaptive image denoising function.)

基于改进的F-S error diffusion算法对彩色图像位数降为8色。其中分配系数采用动态系数(Based on the F-S error diffusion algorithm to improve the color image figures down to 8 colors. The distribution coefficient by using the dynamic coefficient)

基于傅里叶梅林变换亚像元图像配准程序，可以运行。(Fourier Mellin transform based sub-pixel image registration process, you can run.)

java 图形算法实现源代码，有坐标放大缩小。(java graphics algorithm source code, coordinates Zooming.)

粒子滤波跟踪，还算不错的一个demo，调试验证可以通过(Particle filter tracking, fairly good one demo, debug and verify through)

对于白噪声的功率谱分析给出了白噪声的频谱波形(White noise matlab )

这是模式识别中线性分类器中最常用的fisher线性分类器，并在结果中画出了分类线和分类效果(This is image restoration in the Iterative Blind Deconvolution algorithm matlab source for the study of digital image has a lot of benefits!)

　对经典的二值化方法Ostu 算法和Bernsen 算法中存在的缺点进行了分析, 提出一种结合Canny 算子的图像 二值化方法.该方法综合考虑了边缘信息和灰度信息, 通过边缘附近种子点在高阈值二值化图像中的填充和低阈值 图像对它的修补而得到二值化结果图像, 较好地解决了经典二值化方法中存在的抗噪能力差、边缘粗糙、伪影现象 等缺点.实验结果证明, 该方法能够较好地解决低对比度图像和目标像素灰度不均匀图像的二值化问题(The disadvantage of the classical binary method Ostu Bernsen algorithm and algorithms exist were analyzed, one binding Canny operator image binarization method. This method considers the edge information and gray information, through seed near the edge point padding and a low threshold image to its repair in a high threshold binary image to obtain a binary result image, it solves the noise immunity classical binary method exists difference, rough edges, artifacts phenomenon and other shortcomings. the experimental results show that this method can solve the low-contrast image and the target image pixel gray uneven binary problem)

c++实现的sift，可以分步观看实现效果(c++ implementation of the sift, you can watch step by step to achieve results)