搜索资源列表
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混合高斯模型，建立背景模型，从而可以分离前景与背景,Gaussian mixture model, background model, which can be separated from foreground and background
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基于opencv的背景差法，实现前景和背景的分离。需要先配置好vc2008，然后编译执行。-Opencv background difference based method to achieve the separation of foreground and background. Need to configured vc2008, and then compile the implementation.
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是一个实用的抠图软件，软件很小，对图像的前景和背景分离效果很好-Matting is a practical software, software is very small, the image foreground and background separation worked well
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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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程序实现一个自适应阈值的算法，用以在不规则光照下从背景中提取出前景图像。-Procedures to achieve an adaptive threshold algorithm to extract the foreground from the background image in the irregular light.
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通过读取视频流，基于OpenCV建立背景模型，以用于前景检测-By reading the video stream, based on the OpenCV ，generate background model for the foreground pixels detection
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本文通过融合图像的颜色和梯度特征 ,实现了一种实时背景减除方法。首先融合颜色和梯度特征建立新的能量函数 然后基于图切割算法最小化能量函数 ,并对前景P 背景进行分割 最后使用光流验证前景区域的真实性 ,并更新背景模型。- Based on the fusion of color and gradient features , this paper implement s a novel approach to real-time background subtraction.Firstly ,
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可实现与graph-cuts算法相似的图像分割效果. 他借用了生物形态学知识，将每一个像素视为一个细胞，这些细胞可能是前景，背景，或其他。这些细胞依据其灰度竞争获得生长，由此获得分割。-This algorithm is presented as an alternative to graph-cuts. The operation is very simple, and can be thought of with a biological metaphor: Imagine each ima
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通过单高斯来建立背景，然后用背景减法来提取情景，并对前景进行跟踪和计数。-We establis the background through the method of singal Gaussian,then use the background substration to get foreground,we are also successed to get the counts of cars and to track the cars.
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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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迭代法求阈值的原理：
基于逼近的思想，步骤如下：
1． 求出图象的最大灰度值和最小灰度值，分别记为ZMAX和ZMIN，令初始阈值T0=(ZMAX+ZMIN)/2；
2． 根据阈值TK将图象分割为前景和背景，分别求出两者的平均灰度值ZO和ZB
3． 求出新阈值TK+1=(ZO+ZB)/2；
4． 若TK=TK+1，则所得即为阈值；否则转2，迭代计算。
- Iteration method threshold principle: based on the i
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自己修改的基于closed-form的程序，对外国程序在时间上有两倍的改善，能对在复杂背景下的图片，通过画一些简单的前景线，背景线，抠图出前景-it is about matting foreground when the interested ared is located under complixed background,and then choosing some other beautiful background,you can create new image,which bri
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Real-time foreground–background segmentation
using codebook mode-Real-time foreground-background segmentation using codebook mode
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研究一种红外医学图像处理与分析方法,实现红外人脸图像中特征区域的自动定位。方法
针对红外正面脸部图像,采用一种无监督的局部和全局的特征提取方法,首先通过阈值法区分出前景和
背景,并根据面部特征对称性在前景中确定鼻区 然后在面部确定一个包含所有特征的矩形区域,利用
Harris算子在该区域检测出角点,并找出这些点的局部最大值点 最后用K-means方法对这些点进行
聚类
-To develop an mi age analyzing procedure forautomatic
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Tracking w/ blob detection, morphological operation (Togeather)
frames = {avi.cdata} uses the cdata from the video file
fg = extractForeground(frames) do foreground extraction
cmap = colormap(gray)
for i = 1:length(fg)
temp0{i} = edg
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A better version of guassian mixture model for background subtraction and foreground detection, using matlab and c language to implement it.
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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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复杂背景下的前景检测，这篇文章被引用的比较多，值得学习-foreground detection from video in complex background
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前景提取四种方法，好学易懂，适合初学者！(Foreground extraction)
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