xiaobozengqiang

数学建模，动态模糊与模糊图像处理，维纳滤波方法。(Mathematical modeling, dynamic fuzzy and fuzzy image processing, Wiener filtering method.)

实现一个信号的WVD变换，针对的信号是非平稳的随机信号(To achieve a WVD transformation of a signal, the signal is a non-stationary random signal.)

非局部均值滤波 输入: 待平滑的图像 t: 搜索窗口半径 f: 相似性窗口半径 h: 平滑参数 NLmeans(ima,5,2,sigma) (Non-local mean filtering input: to be a smooth image t: the search window radius f: similarity of the window radius h: smoothing parameter NLmeans (ima, 5,2, sigma) )

说明：  这是“An efficientlocalChan–Vesemodelforimagesegmentation”（简称LCV模型）的MATLAB源代码。LCV模型是非常重要局部区域活动轮廓模型，它被广泛使用于各个领域，如MRI大脑图像分割，血管图像分割，图像偏差场纠正。(This is "An efficientlocalChan-Vesemodelforimagesegmentation" (referred to as the LCV model) of the MATLAB source code. LCV model is very important to local area active contour model, which is widely used in various fields, such as MRI brain image segmentation, vessel segmentation, image bias field correction.)

一种基于改进DRLSE的运动目标分割方法,为了得到精准、连续的运动目标轮廓，提出将帧间差分法和改进的距离规则水平集演化(DRLSE)方法结合应用于运动目标分割。(In order to get an accurate and continuous contour of moving target, a novel method to segment moving object by combining the frame difference and an improved distance regularized level set evolution (DRLSE) is proposed. )

改进后的非线性各向异性扩散用于图像中的保边去噪，增加了二阶导数，是滤波效果更好(Improved nonlinear anisotropic diffusion for edge preserving image denoising, an increase of second derivative, is a better filtering effect)

遥感影像处理，可以用于盲源信号分离，快速高效的独立成分分析方法(Remote sensing image processing, can be used for blind source separation, rapid and efficient method of independent component analysis)

说明：  Kirsch算子边缘检测代码，计算了八个方向的梯度值(Kirsch operator edge detection code)

说明：  最大熵双阈值分割算法，基于遗传算法优化的快速最大熵双阈值分割算法。(Maximum Entropy Double Threshold Segmentation and Fast Maximum Entropy Double Threshold Segmentation Based on Genetic Algorithms.)

对输入视频进行处理，使用高斯背景建模的方法，得到视频前景，对于人群密度估计具有很大作用。(Processing of the input video, using a Gaussian background modeling method, to get videos prospects for population density estimation has a significant role.)