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1.Yasutaka Furukawa[ 雅书塔卡·富鲁卡瓦 ] （华盛顿大学和伊利诺伊香槟分校）
http://www.cse.wustl.edu/~furukawa/
2.华盛顿大学图形与图像实验室
http://grail.cs.washington.edu/
3.米德伯视觉实验室
http://vision.middlebury.edu/
4. 法国国立路桥大学校图像组
http://imagine.enpc.fr/index.html
5. 法国国家信息与自动化研究所 Florent Lafarge [ 弗洛伦特·拉法基 ]
http://www-sop.inria.fr/members/Florent.Lafarge/
6.香港城市大学 Qingxiong Yang
http://www.cs.cityu.edu.hk/~qiyang/publications.html
7.苏黎世联邦理工视觉实验室
https://cvg.
SLAM领域牛人、牛实验室、牛研究成果梳理
点击公众号“计算机视觉life”关注，置顶星标更快接收消息！ 本文阅读时间约5分钟 对于小白来说，初入一个领域时最应该了解的当然是这个领域的研究现状啦。只有知道这个领域大家现在正在干什么，才能知道自己应该做什么。关注领域内的大牛以及领域内比较著名的实验室，紧跟大牛的脚步，才能走在科研的最前沿。今天CV_life君就帮各位整理了一些现阶段国内外SLAM的著名实验室，大牛以及研究成果，还会附带大牛...
中科院自动化所三维重建PPT(完整版)
中科院自动化所三维重建PPT(完整版) 计算机视觉 slam 必备
Multicore-TSNE, 用 python 和 Torch 封装实现并行的实现.zip
Multicore-TSNE, 用 python 和 Torch 封装实现并行的实现 多核t sne 这是对 Barnes的多多核改造，by 。 带有 python 和 Torch cffi的Van der Maaten 。 这里代码在内核上比 sklearn.TSNE的工作速度还要快。 ：预期的内容Barnes
graphcut代码
Yuri Boykov ， Vladimir Kolmogorov某文中代码
NoUnityCN：绕过地域限制，从Unity官方CDN直链下载全版本编辑器
在软件开发和游戏制作领域，内容分发网络（CDN）是保障全球用户高速、稳定获取大型文件的核心基础设施。其原理是通过将资源缓存至遍布各地的边缘节点，使用户能从地理距离最近的服务器获取数据，从而显著提升下载速度并降低源站压力。这一技术对于需要频繁下载大型开发工具（如游戏引擎）的团队而言，价值在于能确保开发环境部署的一致性与效率，是构建自动化CI/CD流水线和团队协作的基础。然而，当开发者因网络策略或地域限制无法直接访问官方CDN时，获取安装文件便成为阻碍工作流的痛点。NoUnityCN项目正是针对此场景的工程实践
集成calibre到cadence virtuoso环境中 load: can‘t access file - “/opt/eda/Mentor/lib/calibre.skl“
项目场景： 设想是将Calibre集成到cadence virtuoso中，但网上的解决办法尝试过在本机上实验都有点问题，因此想记录下来我的解决办法仅供参考。 问题描述： 安装好cadence， calibre之后打开virtuoso，CIW窗口显示 *Error* load: can't access file - "/opt/eda/Mentor/lib/calibre.skl" 问题是出在calibre.skl的加载路径出错，尝试网上的在工程目录下修改.cdsinit文件，添加calibre安装
制作3D地形图的方法-使用GlobalMapper和3ds Max软件
打开GlobalMapper软件，在菜单栏中选择“文件”>“打开数据文件”，然后选择相应的高程数据文件。在菜单栏中选择“渲染”>“渲染设置”，根据需要进行设置，如分辨率和输出格式。选择地形对象，点击菜单栏中的“渲染”>“材质编辑器”，然后根据需要调整材质属性，如颜色、纹理和光照等。在菜单栏中选择“工具”>“高程栅格和网格”>“创建地形网格”。在菜单栏中选择“文件”>“导出”>“导出栅格格式”。打开3ds Max软件，在菜单栏中选择“文件”>“导入”，然后选择之前导出的地形数据文件。步骤3：导出地形网格。
安卓新奇玩机工具全攻略___联发科MTK v6 限时免费 刷写固件工具操作
摘要：该文介绍一款限时免费的联发科V6刷机工具，支持小米、Vivo、Moto等多品牌设备，包括红米Note14Pro等机型（Klimt/Goya等代码）。工具需注册使用，提供EFS分区备份功能。作者提示工具可能触发杀毒警报，强调禁止非法用途，并说明部分联网工具可能失效。
智能车图像处理5-入门级分析4 ---环岛的状态机与补线
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家用智能监控摄像机如何使用NAS进行存储/备份（小米智能摄像机如何解决群晖 / 飞牛 NAS 识别失败、文件目录不显示问题）
小米摄像头NAS存储总出问题？🤯 别慌！本文揭秘NAS底层逻辑，群晖、飞牛实战指南。手把手教你排查解决所有难题，轻松搞定监控录像安全备份！🔒
电磁场仿真软件：Sonnet_（3）.Sonnet用户界面与操作
Sonnet支持用户通过二次开发自定义工具，例如：编写Python脚本，实现自定义功能。将脚本保存为工具插件。在Sonnet中加载并使用自定义工具。# 示例：自定义工具 import sonnet def custom_tool(design , object , parameter , value) : """自定义工具示例，设置对象的指定参数。
Oracle 查看 rman 备份情况 SQL 语句
每天一个 DBA 小知识，助你更进一步！
visio安装包
Microsoft Office Visio 是一款专业的制图软件。利用经过更新的形状、协作工具和数据链接图表，创建专业级图表以简化复杂的信息。
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
SIMATIC S7-1200, S7-1500 PID 控制[手册]
SIMATIC S7-1200, S7-1500 PID 控制[手册]
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