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BMatting
- 对贝叶斯抠图的思想进行实现，VC下编的，可以对照片进行前景抠图
src
- implement paper A Bayesian Approach to Digital Matting 贝叶斯抠图 matlab code
A Bayesian Approach to Digital Matting
- 贝叶斯抠图过程的中文解释，详细说明贝叶斯抠图的过程原理
BMatting
- 对贝叶斯抠图的思想进行实现，VC下编的，可以对照片进行前景抠图-Cutout of Bayesian thinking to achieve, VC under, and can carry out the prospect of Photo Cutout
src
- implement paper A Bayesian Approach to Digital Matting 贝叶斯抠图 matlab code-implement paper A Bayesian Approach to Digital Matting Bayesian Cutout matlab code
Bayes-Matting
- 抠图中最为经典和基本的算法贝叶斯抠图，在matlab下的实现，效果和速度都较为理想。-Bayessian Matting is the most basic and classic method in matting algorithm.We provide a implementation in matlab.
chap1_5
- 贝叶斯抠图，依次打开原图和trimap图，点抠图即可运行。绝对能够运行，另外就是有些调试信息没有去除，大家运行时不用理就行。再鄙视那些上传源码不完整的，浪费人感情！-Bayesian matting, and then click open the original image and the diagram, point matting to run. Definitely be able to run, the other is n
BayesianMatting
- 这个代码主要针对贝叶斯抠图的一个程序！其中包括模式识别！-This code for a Bayesian matting program! Including pattern recognition!
Matting(M7)
- 这个程序是用的贝叶斯来进行抠图！可以是彩色图也可以是灰度图！-This program is using Bayesian matting! May be a color image can also be grayscale!
spectralMattingSupplementary
- 这个程序主要是用来数字抠图！方法是贝叶斯！-This program is used digital matting! The method is Bayesian!
matting-A-
- 这个程序主要是用来数字抠图！方法是贝叶斯！-This program is used digital matting! The method is Bayesian!
spectralMattingCode
- 这个程序主要是针对数字图像抠图的！用的是贝叶斯的方法！-This program is mainly for digital image matting! Bayesian method!
bayes-possion-robust-flash-matting
- 集成了贝叶斯抠图、泊松抠图、鲁棒抠图的程序-Bayesian matting program
bayes
- 基于贝叶斯算法的数字图图像抠图，在matlab下实现其运行，图像保存在目录为e盘-Based on bayesian algorithm of digital map image cutout, achieve its operation under matlab, to e disk images stored in the directory
bayesian-matting-master
- 本文将以图像抠图领域的经典算法——贝叶斯抠图（Bayesian Matting）为例来介绍有关图像抠图技术的一些内容。贝叶斯抠图源自文献【2】，是2001年发表在CVPR上的一篇经典论文。(the image matting using classic bayesian approach)
Bayesian-Matting-master
- 贝叶斯框架抠图,高斯分布，采样，最大似然率，三分图，前景区域，北京区域，未知区域(proposes a new Bayesian fr a mework for solving the matting problem, i.e. extracting a foreground element from a background image by estimating an opacity for each pixel of the