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opencv实现的snake算法,可以用于轮廓提取。,opencv realized snake algorithm, can be used for contour extraction.
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本程序以VC++为开发环境,以opencv和MFC为开发语言实现用Snake方*廓提取轮廓。提取轮廓是图像处理和视频流处理的基础...,This procedure for a VC++ development environment, and MFC in opencv using the development of language Snake contour contour extraction method. Extraction of the outline of the imag
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利用OPENCV,切运用CANNY算法对一副图像进行轮廓提取-Use OPENCV, cut algorithm using CANNY image contour extraction of a
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opencv实现图像的轮廓提取。通过函数载入一幅图像,分析图像并获取图像的轮廓。-opencv of image contour extraction. By loading an image, the outline of the image analysis and obtain
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C++做的边缘提取算法,要利用OPENCV-contour extraction
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用opencv在VC+++环境下实现对图像轮廓的提取,为进一步的图像分割、识别做准备-Using opencv in VC+++ environment to realize the image contour extraction for further image segmentation, recognition in preparation
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此代码借助OpenCV开源计算机视觉库提取轮廓等技术实现细胞数的统计-With this code, open source computer vision library OpenCV contour extraction techniques to achieve the statistical cells
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用OPENCV实现的活动轮廓提取,速度较快,对于简单物体轮廓提取效果良好-OPENCV achieved with active contour extraction, faster, for the simple contour extraction well
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OpenCV—RGB图像灰度化,并提取轮廓-OpenCV-RGB gray-scale images and contour extraction
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利用OpenCv实现图像中目标的轮廓提取,针对二值图像。-The use of OpenCv to achieve the target in the image contour extraction for binary image.
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轮廓检出 复杂背景下的物体轮廓检出。利用opencv函数将所有轮廓抽出后根据面积取最大轮廓抽出-Contour detection of object contours detection in complex backgrounds. Using opencv function will take out all the contours of the maximum contour extraction based on area
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图像处理中使用的轮廓提取,采用opencv,c++编写-Contour extraction using image processing using opencv, c++ write
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OpenCV 的圆形标记点的提取方法, 通过对图像进行平滑处理 、 边缘检测 、 轮
廓查找 、 几何约束以及椭圆拟合, 实现圆形标记点的提取。 有关圆形物体识别的场合。 -Extraction OpenCV circular markers by the image smoothing, edge detection, contour search, geometric constraints and ellipse fitting, to achieve extraction of ci
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实现了树叶轮廓的提取以及深度信息的提取,使用opencv-Contour extraction achieved leaves and extracting depth information, using opencv
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边缘检测,轮廓提取基本操作,VS 2013 64位仅供学习(Edge detection, outline extraction basic operation, VS 201364 bits for learning only)
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