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本代码是Foreground-Background Segmentation of Video Sequences的配套程序，利用KDE算法对avi进行处理，从而提取前景和背景，在windows, Visual C++下测试成功
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This code implements the well-known Chan-Vese segmentation algorithm from the paper \"Active Contours Without Edges.\"
This technique deforms an initial curve so that it separates foreground from background based on the means of the two regions.
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介绍一种应用于视频运动检测中前景和背景建模分割的方法-proposes a novel method for detection and segmentation
of foreground objects from a video which contains
both stationary and moving background objects and undergoes
both gradual and sudden “once-off” changes.
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光流法之一：codebook BGS代码，参考论文Real-time foreground–background segmentation using codebook model （Kyungnam Kima,, Thanarat H. Chalidabhongseb, David Harwooda, Larry Davis），进行了简化。
实验效果：似乎噪声点太多了。-One of the optical flow method: the BGS codebook code, refer
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两次利用大津法对图像进行分割，将前景与背景，白色区与背景分割开，并二值化。-Otsu method twice using image segmentation, the foreground and background, white background area and separate, and binary.
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基于区域融合的半监督的图像分割算法。首先在背景和前景手动设置初始分割标记，在迭代过程中不断通过区域融合操作获得最大相似度的区域，从而实现目标分割。-Regional integration based on semi-supervised image segmentation. First of all, in the background and foreground segmentation manually set the initial marking, in the iteration
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Real-time foreground–background segmentation
using codebook mode-Real-time foreground-background segmentation using codebook mode
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grab cut的源码，grab cut是一种效果比较好的交互式前景和背景分割方法。-grab cut the source, grab cut is a good interactive effect of the foreground and background segmentation.
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迭代法是基于逼近的思想，逼近的目标是使得：前景和背景的平均灰度值的平均值即为阈值。该方法的原理是：如果用某一阈值分割出的图像，其两部分平均值的中值，正好等于该阈值，那么这个阈值就是所求的阈值。-Iterative method is based on the approximation of the idea of approaching the goal is to make: the foreground and background is the average
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Real-time foreground–background segmentation using codebook model
了解码书进行图像处理(背景与前景)的原理，可以从该文章中获得有益的参考价值-Real-time foreground–background segmentation using codebook model
A practical paper for knowing of image processing theory(about foregro
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分五个部分讲解：1.计算分割函数。图像中较暗的区域是要分割的对象。
2.计算前景标志。这些是每个对象内部连接的斑点像素。
3.计算背景标志。这些是不属于任何对象的像素。
4.修改分割函数，使其仅在前景和后景标记位置有极小值。
5.对修改后的分割函数做分水岭变换计算。-Divided into five sections explain: (1) Calculate the partition function. The dark areas in the image is the
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交互式支持向量机图像分割程序，通过鼠标选取区域实现前景和背景选取-Interactive support vector machine image segmentation procedures, through the mouse to select regions of foreground and background selection
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【原创】自已写的采用自适应背景差法分割前景图像的程序，中间使用了LBP背景消除方法和区域生长算法，经验证效果相当好。-[original]Segmentation of the foreground image using adaptive background subtraction . In the program, LBP background elimination method and region growing algorithms is used.
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自动前背景分割，基于超像素、形状和外观前验概率及图切割-A novel foreground/background segmentation
algorithm that attempts to segment the interesting
objects from the rest of the image.The model includes a geometric prior and an appearance
prior, whose parameters are lear
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背景建模和前景分割的方式把运动车辆提取出来。并进行最近临关联，输出目标轨迹。
MeanShift运动目标跟踪 matlab程序
1.截取跟踪目标矩阵rect
2.求取跟踪目标的加权直方图hist1
3.读取视频序列中的一帧， 先随机取一块与rect等大的矩形，计算加权直方图hist2。
4.计算两者比重函数，如果后者差距过大， 更新新的矩阵中心Y，进行迭代（MeanShift是一种变步长可以迅速接近概率密度峰值的方法），直至一定条件后停止。
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利用基于直方图的自适应阈值方法实现分割前景与背景-To achieve segmentation foreground and background using histogram-based adaptive threshold method
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基于vs2008和opencv2.4.2的图像分割程序，该程序实现从给定图像中分割出前景ROI并显示分割后的结果。包含矩形窗口分割和掩码图像分割两种模式，采用掩码图像分割时，按Ctrl和鼠标左键添加背景，按Shift键和鼠标左键添加前景-Image segmentation based on vs2008 and opencv2.4.2 program, the program implementation from a given image segment the foreground RO
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基于直方图的自适应阈值法实现分割图像前景与背景，matlab编写，包含测试图像和实验报告-Foreground and background to achieve image segmentation histogram-based adaptive threshold method, matlab prepared, containing the test images and test reports
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使用opencv进行Crabcut图像分割，先定义分割矩形区域，然后完成前景与背景图像分离-Opencv for image segmentation using Crabcut, to define rectangular areas divided, and complete separation of foreground and background image
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主要是用于图像的分割，具体的算法不详，但是当前景和背景的区分度稍微高时，分割效果是相当不错的，但是如果区分度不是很高，效果就稍微差一点；(the image segmentaion for image matting which is suitable for images which has a high contrast between foreground and background;)
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