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danmu

于 2020-03-23 发布
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说明：  基于opencv平台编写的单目相机标定程序，使用的张正友的方法，只要准备十二张单目相机拍摄的棋盘格的图片就可以标定出单目相机的焦距等参数，建立单目相机坐标系。可以用于机器视觉测量中，也可以用于双面相机标定的前一步，精度很高。(Based on the monocular camera calibration program compiled by the OpenCV platform, the method of Zhang Zhengyou is used to calibrate the focal length and other parameters of the monocular camera and establish a monocular camera coordinate system as long as 12 pieces of chessboard pictures taken by the monocular camera are prepared. It can be used in machine vision measurement, and it can also be used in the previous step of the calibration of the double-sided camera, with high precision.)

文件列表：

danmu\.vs\danmu\v15\.suo, 27136 , 2020-01-30
danmu\.vs\danmu\v15\Browse.VC.db, 3284992 , 2020-01-30
danmu\.vs\danmu\v15\ipch\AutoPCH\DANMU-d6674eff\DANMU-3784a836\DANMU.ipch, 21561344 , 2020-01-30
danmu\danmu\danmu.cpp, 6137 , 2016-08-15
danmu\danmu\danmu.vcxproj, 4766 , 2020-01-30
danmu\danmu\danmu.vcxproj.filters, 943 , 2016-03-01
danmu\danmu\danmu.vcxproj.user, 221 , 2020-01-30
danmu\danmu\Debug\cl.command.1.tlog, 654 , 2016-08-15
danmu\danmu\Debug\CL.read.1.tlog, 20236 , 2016-08-15
danmu\danmu\Debug\CL.write.1.tlog, 340 , 2016-08-15
danmu\danmu\Debug\danmu.Build.CppClean.log, 2424 , 2016-08-15
danmu\danmu\Debug\danmu.exe.embed.manifest, 406 , 2016-08-15
danmu\danmu\Debug\danmu.exe.embed.manifest.res, 472 , 2016-08-15
danmu\danmu\Debug\danmu.exe.intermediate.manifest, 381 , 2016-08-15
danmu\danmu\Debug\danmu.lastbuildstate, 76 , 2016-08-19
danmu\danmu\Debug\danmu.log, 885 , 2016-08-19
danmu\danmu\Debug\danmu.obj, 114216 , 2016-08-15
danmu\danmu\Debug\danmu.vcxprojResolveAssemblyReference.cache, 707 , 2016-08-19
danmu\danmu\Debug\danmu.write.1.tlog, 0 , 2016-08-15
danmu\danmu\Debug\danmu_manifest.rc, 200 , 2016-08-15
danmu\danmu\Debug\link-cvtres.read.1.tlog, 2 , 2016-08-15
danmu\danmu\Debug\link-cvtres.write.1.tlog, 2 , 2016-08-15
danmu\danmu\Debug\link.18556-cvtres.read.1.tlog, 2 , 2016-04-07
danmu\danmu\Debug\link.18556-cvtres.write.1.tlog, 2 , 2016-04-07
danmu\danmu\Debug\link.18556.read.1.tlog, 2 , 2016-04-07
danmu\danmu\Debug\link.18556.write.1.tlog, 2 , 2016-04-07
danmu\danmu\Debug\link.5376-cvtres.read.1.tlog, 2 , 2016-08-15
danmu\danmu\Debug\link.5376-cvtres.write.1.tlog, 2 , 2016-08-15
danmu\danmu\Debug\link.5376.read.1.tlog, 2 , 2016-08-15
danmu\danmu\Debug\link.5376.write.1.tlog, 2 , 2016-08-15
danmu\danmu\Debug\link.command.1.tlog, 1836 , 2016-08-15
danmu\danmu\Debug\link.read.1.tlog, 4060 , 2016-08-15
danmu\danmu\Debug\link.write.1.tlog, 744 , 2016-08-15
danmu\danmu\Debug\mt.command.1.tlog, 372 , 2016-08-15
danmu\danmu\Debug\mt.read.1.tlog, 164 , 2016-08-15
danmu\danmu\Debug\mt.write.1.tlog, 310 , 2016-08-15
danmu\danmu\Debug\rc.command.1.tlog, 508 , 2016-08-15
danmu\danmu\Debug\rc.read.1.tlog, 282 , 2016-08-15
danmu\danmu\Debug\rc.write.1.tlog, 290 , 2016-08-15
danmu\danmu\Debug\vc100.idb, 543744 , 2016-08-15
danmu\danmu\Debug\vc100.pdb, 905216 , 2016-08-15
danmu\danmu\Distortion-l.txt, 271 , 2016-08-19
danmu\danmu\Distortion-r.txt, 270 , 2016-08-19
danmu\danmu\Distortionl (2).txt, 270 , 2016-04-07
danmu\danmu\Distortionl.txt, 270 , 2016-04-07
danmu\danmu\Distortionr (2).txt, 270 , 2016-04-07
danmu\danmu\Distortionr.txt, 270 , 2016-04-07
danmu\danmu\Intrinsics-l.txt, 267 , 2016-08-19
danmu\danmu\Intrinsics-r.txt, 267 , 2016-08-19
danmu\danmu\Intrinsicsl (2).txt, 267 , 2016-04-07
danmu\danmu\Intrinsicsl.txt, 267 , 2016-04-07
danmu\danmu\Intrinsicsr (2).txt, 267 , 2016-04-07
danmu\danmu\Intrinsicsr.txt, 267 , 2016-04-07
danmu\danmu\list.txt, 238 , 2016-08-19
danmu\danmu.sdf, 13651968 , 2016-08-19
danmu\danmu.sln, 882 , 2016-02-29
danmu\danmu.suo, 12800 , 2016-08-19
danmu\Debug\danmu.exe, 39936 , 2016-08-15
danmu\Debug\danmu.ilk, 394596 , 2016-08-15
danmu\Debug\danmu.pdb, 1248256 , 2016-08-15
danmu\ipch\danmu-3c430f66\danmu-25a88cc1.ipch, 57081856 , 2016-08-19
danmu\ipch\danmu-561645db\danmu-25a88cc1.ipch, 57081856 , 2016-04-07
danmu\单目原理.doc, 243200 , 2016-02-29
danmu\单目标定.doc, 33280 , 2016-02-26
danmu\输出-生成.txt, 889 , 2016-03-01
danmu\输出-调试.txt, 8156 , 2016-03-02
danmu\.vs\danmu\v15\ipch\AutoPCH\DANMU-d6674eff\DANMU-3784a836, 0 , 2020-01-30
danmu\.vs\danmu\v15\ipch\AutoPCH\DANMU-d6674eff, 0 , 2020-01-30
danmu\.vs\danmu\v15\ipch\AutoPCH, 0 , 2020-01-30
danmu\.vs\danmu\v15\ipch, 0 , 2020-01-30
danmu\.vs\danmu\v15, 0 , 2020-01-30
danmu\.vs\danmu, 0 , 2020-01-30
danmu\danmu\Debug, 0 , 2017-04-17
danmu\danmu\image, 0 , 2020-03-23
danmu\ipch\danmu-3c430f66, 0 , 2017-04-17
danmu\ipch\danmu-561645db, 0 , 2017-04-17
danmu\.vs, 0 , 2020-01-30
danmu\danmu, 0 , 2017-04-17
danmu\Debug, 0 , 2017-04-17
danmu\ipch, 0 , 2017-04-17
danmu, 0 , 2020-01-30

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0 个回复

  • yj
    说明：  ：针对显微图像分析、识别需要的全自动控制显微镜，研究开发其相应的图像处理算法。提 出了能量谱自动聚焦评价函数算法、自适应选取基准图的图像拼接算法、改进 Laplacian算子的多层 聚焦图像叠合算法等。该算法模块已成功地应用于所开发的CMIAS 显微医学图像分析系统中，取得 了满意的应用效果。(【ABSTRACT】For the automatic microscope required by micro-image analysis and recognition, the relevant image processing a lgorithms are investigated. Three algorithms are described: energy-spectrum image auto focusing evaluation function, image merging based on the adaptive selection of standard image and sum-modified-Laplacian (SML) operator for multi-focus image fusion. The algorithms have been applied successfully to CMIAS medical micro-optical image analysis system and testified to be feasible and effective. )
    2009-11-05 10:02:07下载
    积分：1
  • visual-cPP-image-process
    数字图像处理诸多算法源代码，图像处理的同学可以下载下来看看，适合初学者。(c++ code of digital image process )
    2012-03-16 16:52:53下载
    积分：1
  • Edge-Thinning
    Gray Level Edge Thinning Method Abstract. An effective edge thinning algorithm is important in image segmentation and object identification since it increases the possibility of success in detecting objects in the image and saves the processing time in the further steps such as labeling and image transformation. We investigate some gray level edge thinning method that is based on the gradient magnitude within 3x3 local neighborhood. The new method has been tested with various types of images the real world. Experimental results show that this method produces more accurate images than those of the popular non-maximum suppression methods do.
    2016-09-13 22:05:34下载
    积分：1
  • xiaobo
    结合维纳滤波的小波降噪方法，滤波效果很好(Combination of Wiener filtering of wavelet noise reduction method, filter works well)
    2009-10-24 13:59:40下载
    积分：1
  • MbufAllocColor
    这个函数分配一个数据缓冲区与多个颜色指定系统上的乐队。这种类型的缓冲区允许表示彩色图像(例如,RGB)。 这个函数为每个指定分配缓冲区,一个二维表面颜色乐队。您可以使用MbufAlloc1d()和MbufAlloc2d()来创建单独的乐队一或二维 数据缓冲区,分别。 分配一个缓冲区后,我们建议您检查是否操作成功,使用MappGetError(),或通过验证,不是M_NULL返回的缓冲区的标识符。(This function allocates a data buffer with multiple color bands on the specified system. This type of buffer allows for the representation of color images (for example, RGB). This function allocates buffers that have a two-dimensional surface for each specified color band. You can use MbufAlloc1d() and MbufAlloc2d() to create single band one- or two-dimensional data buffers, respectively. After allocating a buffer, we recommend that you check if the operation was successful, using MappGetError(), or by verifying that the buffer identifier returned is not M_NULL.)
    2015-12-03 19:55:48下载
    积分：1
  • mi_registration
    2维的基于互信息的图像配准算法，使用powell法作为优化算法(2-dimensional mutual information based image registration algorithms, the use of law as the optimization algorithm powell)
    2020-06-30 10:20:01下载
    积分：1
  • ICP-point-cloud-registration
    三维激光点云配准是点云三维建模的关键问题之一。经典的 ICP 算法对点云初始位置要求较高且配准 效率较低，提出了一种改进的 ICP 点云配准算法。该算法首先利用主成分分析法实现点云的初始配准，获得较好 的点云初始位置，然后在经典 ICP 算法的基础上，采用 k － d tree 结构实现加速搜索，并利用方向向量夹角阈值去除 错误点对，提高算法的效率。实验表明，本算法流程在保证配准精度的前提下，显著提高了配准效率。 (Three-dimensional laser point cloud registration is one of the key three-dimensional point cloud model. High classical ICP algorithm to the initial position of the point cloud registration requirements and low efficiency, proposed an improved ICP point cloud registration algorithm. Firstly, the use of principal component analysis of the initial point cloud registration, get a better initial position of the point cloud, then the basis of classical ICP algorithm using k- d tree structure to achieve speed up the search, and using the direction vector angle the removal of the threshold point error and improve the efficiency of the algorithm. Experiments show that the algorithm processes to ensure the accuracy of registration under the premise, significantly improve the efficiency of registration.)
    2016-08-01 10:34:57下载
    积分：1
  • rayTracing-1
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    2012-04-23 22:31:49下载
    积分：1
  • Moravec
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    2014-09-03 20:18:45下载
    积分：1
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    2020-12-31 14:58:59下载
    积分：1
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