Search - foreground

[Special Effects] otsu-method

Description: 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.
Platform: | Size: 2048 | Author: budongwei | Hits:

[Special Effects] InteractiveImageSegmentationbasedonMergingRegion.r

Description: 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 process continued operation through regional integration greatest similarity region, to achieve object segmentation.
Platform: | Size: 840704 | Author: 史思琦 | Hits:

[Books] algo_tracking

Description: 学习图像处理和视频监控的入门书籍，介绍了经典的光流法来检测前景和背景内容。-Learning image processing and video surveillance of the entry-book, introduces the classical optical flow method to detect the foreground and background elements.
Platform: | Size: 140288 | Author: yuyongfang | Hits:

[Video Capture] MovingDetection

Description: 用Opencv实现的一个运动检测的程序。通过摄像头捕捉，把捕捉到的图像分为前景和背景，从而检测出运动图像。-Achieved with Opencv a motion detection process. Captured by the camera, the captured image are separated into foreground and background, to detect the moving image.
Platform: | Size: 3857408 | Author: 蓝天白雪 | Hits:

[VC/MFC] beijingenzhong

Description: !本文提出了一种静止摄像机条件下的运动目标检测与跟踪算法,它以一种改进的自适应混合高斯模型为背景更新方法-用连通区检测算法分割出前景目标-以./01/2滤波为运动模型实现对运动目标的连续跟踪-! This paper, a stationary camera for moving target detection and tracking algorithm, based on a modified adaptive Gaussian mixture model for the background updating- with Components of the segment the foreground object detection algorithm- to ./01/2 filter for the motion model to achieve continuous tracking of the moving target
Platform: | Size: 495616 | Author: xxx | Hits:

[Special Effects] diedaifa

Description: 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 ideological approach, the steps are as follows: 1. Images obtained the maximum and minimum gray value of gray value, denoted ZMAX and ZMIN, so that initial threshold value T0 = (ZMAX+ ZMIN)/2 2. TK under the threshold will be divided into foreground and background images were obtained between the average gray value ZO and ZB 3. Calculate the new threshold TK+1 = (ZO+ ZB)/2 4. If TK = TK+1, then the proceeds shall threshold otherwise turn 2 iteration.
Platform: | Size: 1024 | Author: qing | Hits:

[matlab] Snake

Description: 原始snake matlab 源碼，簡單版- seg = localized_seg(I,init_mask,max_its,rad,alpha,method) Inputs: I 2D image init_mask Initialization (1 = foreground, 0 = bg) max_its Number of iterations to run segmentation for rad (optional) Localization Radius (in pixels) smaller = more local, bigger = more global alpha (optional) Weight of smoothing term higer = smoother method (optional) selects localized energy 1 = Chan-Vese Energy 2 = Yezzi Energy (usually works better)
Platform: | Size: 140288 | Author: 王節宣 | Hits:

[matlab] gehanggeliesaomiao

Description: 基于rgb颜色模型的隔行隔列扫描可用于判断背景和前景的区别大小-Rgb color model based on interlaced scanning can be separated out for the difference between background and foreground to judge the size of
Platform: | Size: 89088 | Author: 王晓涛 | Hits:

[Special Effects] WQ_matting_imerode

Description: 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 brings you some special feelings.thanks.
Platform: | Size: 29696 | Author: yandong | Hits:

[Graph program] CodeBookBackground

Description: Real-time foreground–background segmentation using codebook mode-Real-time foreground-background segmentation using codebook mode
Platform: | Size: 1106944 | Author: sunny | Hits:

[Special Effects] Harris

Description: 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 localization of facial fea- tures on infrared mi ages.M ethodsAn unsupervised localand global features extractionmethodwas adopted for the localization of facial features of frontalview face mi age. First, a thresholdwas used to segment the mi age into foreground and background, and the nose was localized by considering the symmetry of the face. Second, Harris operatorwas adopted to detect interest points in a rec- tangulararea covering allthe facial features, and then localmaxmi um of the interestpointswere de- tected. And finally, K-means clusteringmethodwas used to cluster the points and obtain the facial features localization.
Platform: | Size: 165888 | Author: 高雪 | Hits:

[Special Effects] Low-complexity-background-subtraction-using-frame

Description: 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} = edge(fg{i}, canny , 0.99) + fg{i} temp2 = temp0{i} temp2 = cat(3,temp2,temp2,temp2) fgs = rgb2gray(temp2) sedisk = strel( square ,10) fgs = imclose(fgs, sedisk) fgs = imfill(fgs, holes ) RLL = bwlabel(fgs) stats = regionprops(RLL, basic , Centroid ) fig = figure(1),imshow(RLL) hold on for n = 1:length(stats) if(stats(n).Area > 100) plot(stats(n).Centroid(1), stats(n).Centroid(2), r* ) end end hold o-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} = edge(fg{i}, canny , 0.99) + fg{i} temp2 = temp0{i} temp2 = cat(3,temp2,temp2,temp2) fgs = rgb2gray(temp2) sedisk = strel( square ,10) fgs = imclose(fgs, sedisk) fgs = imfill(fgs, holes ) RLL = bwlabel(fgs) stats = regionprops(RLL, basic , Centroid ) fig = figure(1),imshow(RLL) hold on for n = 1:length(stats) if(stats(n).Area > 100) plot(stats(n).Centroid(1), stats(n).Centroid(2), r* ) end end hold off
Platform: | Size: 17408 | Author: sivasankar | Hits:

[matlab] BackgroundSubtractionReview-Piccardi

Description: given a frame sequence from a fixed camera, detecting all the foreground objects : the detection of the foreground objects is obtained by the difference between the current frame and an image of the scene’s static background
Platform: | Size: 281600 | Author: sonda | Hits:

[Special Effects] GauMM_

Description: A better version of guassian mixture model for background subtraction and foreground detection, using matlab and c language to implement it.
Platform: | Size: 153600 | Author: victor | Hits:

[Special Effects] Binaryzation

Description: 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 of the average gray value threshold. The principle is: if a threshold image segmentation, the average value of two parts, exactly equal to the threshold, then the threshold is the threshold requirements.
Platform: | Size: 14336 | Author: 坏蛋 | Hits:

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