GMM

说明：  关于非下采样轮廓波域红外与可见光图像配准算法(On the registration algorithm of infrared and visible image in unsampled contour wave domain)

论文介绍了一种SFS 算法的参数估计及其实现。它在考虑自遮掩影响的情况下, 有效地估计了SFS 算法中涉及的各种控制参数, 并引入亮度约束、灰度梯度约束和可积性约束, 计算出表面高度和表面向量, 实现三维重构。最后还指出了在Matlab 中实现需要注意的问题。(The paper introduced a SFS algorithm parameter estimation and its implementation. It under the condition of considering the covering effect, estimates the SFS algorithm effectively involved in all kinds of control parameters, and introduces brightness constraint, gray level gradient constraint and the integrability of constraints, calculate the surface height and surface vector, realize the three-dimensional reconstruction. Finally, points out the problems requiring attention is realized in Matlab. )

常规的中值滤波器，在噪声的密度不是很大的情况下效果不错。但是当概率出现的概率较高时，常规的中值滤波处理后，仍然具有噪声点，并丢失了细节和边缘，效果不是很好。 自适应中值滤波器可以尽可能的保护图像中细节信息，避免图像边缘的细化或者粗化。(The conventional median filter works well when the noise density is not very large. However, when the probability of probability is high, the conventional median filter still has noise points, and the details and edges are missing. The result is not very good. The adaptive median filter can protect details in the image as far as possible and avoid thinning or coarsening of the image edges.)

对图像进行伽马校正，可以有效提升图像 亮度和对比度，低于低照度图像现在小于1的参数，对于高亮度的图像选择大于1的参数可以有效调节图像亮度。(Gamma correction can effectively improve the brightness and contrast of the image. The parameters lower than the low illumination image are less than 1. For the high brightness image, the selection of parameters greater than 1 can effectively adjust the brightness of the image.)

前景检测 对动态背景进行多角度监测 动态背景检测(Foreground detection is used to monitor dynamic background from different angles)

压缩包展示了对含高斯噪声和椒盐噪声图片去噪处理的基本去噪演示，包括均值滤波、中值滤波、维纳滤波、小波滤波。希望可以对初学的朋友有所帮助。(Basic denoising with Gaussian noise and salt and pepper noise image denoising demo, including mean filter, median filtering, Wiener filtering, wavelet filter)

adaboost的实现算法，可进行人脸的样本训练(implementation of the AdaBoost algorithm, can be a sample of people face training)

最经典之做，保证别人没有上传过 实现文章 “GrabCut" Interactive Foreground Extraction using Iterated Graph Cuts 用graphcut实现图像分割，效果非常好(The most classic to do to ensure that other people do not realize uploaded article GrabCut Interactive Foreground Extraction using Iterated Graph Cuts with graphcut realize image segmentation, has very good results)

骨骼图像增强，对应于冈萨雷斯数字图像处理教程上的五个步骤(Bone image enhancement, digital image processing corresponding to Gonzalez' s five-step Tutorial)

指纹识别 能够快速的识别出指纹库中的指纹，是否与输入的待检测的指纹相同，效果很好(Fingerprint identification can quickly identify the fingerprint of fingerprints, whether the same as the input fingerprint to be detected, the effect is very good)