bianyuantiqu

harris角点检测，并且实现两幅图像特征点的匹配，最终完成图像的拼接(harris corner detection, and realize two image feature point matching, the final completion of image stitching)

图像分解算法，四种方法可选，包括threshold, K means threshold, region growing以及gradient based segmentation(Image decomposition algorithm, four methods available, including the threshold, K means threshold, region growing and gradient based segmentation)

基于小波域的隐马尔可夫树模型的图像去噪方法的实现代码。(Based on the Wavelet Domain Hidden Markov Tree Model Image Denoising realize the code.)

利用四参数随机生成法生成三维多孔介质，以研究黏土、页岩等的微观流动机制(Using QSGS to construct 3D porous media)

对已得到的图像矩阵进行高斯滤波（卷积），可去燥光滑(Gauss filtering for images)

此程序为区域增长经典算法，程序后面有详细注释(This procedure is a classical algorithmfor the regional growth,the procedure followed in detail the Notes)

对于传统去噪模型的大幅改进，之前的模型都会出现阶梯效应的现象，采取了新的模型对图像进行建模，有效改善了图像去噪图像修复等过程中引入的阶梯效应，而且一定程度上对于图像纹理的修复也有很大的作用(For significantly improve the traditional de-noising model, the model will appear before the phenomenon of the staircase effect, to take a new model for image modeling, effectively improved the staircase effect denoising image restoration process introduced, and to a certain extent for texture of repair also has a significant role)

属于图像分割的一种，利用matlab实现CV型,可以当function使用。其中包含图片，可以进行使用。(It belongs to one kind of image segmentation, and uses MATLAB to realize CV type, which can be used as function.It contains pictures, which can be used.)

介绍了粒子滤波目前主要应用领域, 最后对粒子滤波的发展提出了展望。(Describes the particle filter is currently the main application areas, and finally the development of particle filter raised prospects.)

说明：  这是“Implicit Active Contours Driven by Local Binary Fitting Energy”（简称LBF模型）的MATLAB源代码。LBF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 the "Implicit Active Contours Driven by Local Binary Fitting Energy" (referred to as the LBF model) of the MATLAB source code. LBF 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.)