墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
100+累计阅读
193平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
图像空间变换：实现MATLAB GUI中的图像处理
在图像处理中，图像空间变换是一种常见的技术，用于改变图像的外观和特征。MATLAB提供了强大的图像处理工具箱，结合GUI（图形用户界面）的功能，可以方便地实现图像空间变换。本文将介绍如何使用MATLAB GUI进行图像空间变换，并提供相应的源代码。
首先，我们需要创建一个MATLAB GUI窗口来加载和显示图像。以下是创建GUI窗口的源代码：
function imageTransformationGUI
% 创建GUI窗口
fig = uifigure('Name', '图像空间变换', 'Position', [<【图像几何】基于matlab GUI图像空间变换（仿射变换）【含Matlab源码 841期】
图像空间变换（仿射变换） 完整的代码，方可运行；可提供运行操作视频！适合小白！
【图像几何】图像空间变换（仿射变换）【含GUI Matlab源码 841期】
图像空间变换（仿射变换） 完整的代码，方可运行；可提供运行操作视频！适合小白！
【图像检测】基于Hough变换实现形状检测matlab代码
1 简介 Hough变换是图像处理中从图像中识别几何形状的基本方法之一.Hough变换的基本原理在于利用点与线的对偶性,将原始图像空间给定的曲线通过曲线表达形式变为参数空间的一个点.这样就把原始图像中给定曲线的检测问题转化为寻找参数空间中的峰值问题.也即把检测整体特性转化为检测局部特性.本文试图以matlab为工具,通过matlab编程仿真,利用hough变换和曲线拟合算法,在图像空间中识别出三角形,矩形和圆. 经典 Hough 变换检测是通过将图像域中的图形映射到 Hough 参数域内，根据某一解析式
MATLAB智能阅卷系统：答题卡识别与GUI界面实现
图像处理技术是利用计算机对图像进行分析、处理、复原、识别等操作的过程，广泛应用于医疗、天文、安全监控、机器视觉等众多领域。MATLAB作为一种强大的数学计算和仿真平台，其图像处理工具箱（Image Processing Toolbox）提供了丰富的图像处理函数和算法，使复杂的图像处理过程变得简单高效。阈值判断是一种常用的图像处理技术，其目的是将图像的像素值从连续范围转化为离散类别，通常分为前景（目标）和背景。在图像识别、图像分割、目标检测等应用中，阈值判断发挥着重要作用。
MATLAB GUI 图像处理演示程序，超级全的，可以用！
数字图像处理全面的演示程序，界面简洁，可以运用。
用STM32F103C8T6和TB6612驱动模块，从零搭建一辆能避障循迹的智能小车（附完整代码）
本文详细介绍了如何使用STM32F103C8T6和TB6612驱动模块从零搭建一辆能避障循迹的智能小车。内容涵盖硬件选型、电路设计、底层驱动配置以及智能行为算法实现，并附有完整代码示例，帮助开发者快速掌握嵌入式开发的核心技术。
Visual Studio报错TRK0005找不到CL.exe？三步彻底解决环境变量问题
本文详细解析了Visual Studio报错TRK0005找不到CL.exe的根本原因，并提供了三步彻底解决环境变量问题的方案。从验证安装完整性到配置永久环境变量，再到注册表级修复，帮助开发者建立健壮的开发环境配置体系，确保编译链稳定运行。
告别regsvr32！易语言调用大漠插件dm.dll的免注册实战（附Win7/10/11避坑指南）
本文详细介绍了易语言如何免注册调用大漠插件dm.dll的实战方法，通过`dmreg.dll`实现真正的免注册调用，规避了传统`regsvr32`注册方式带来的权限和兼容性问题。文章包含核心原理剖析、易语言实现细节、多系统兼容性方案及高级应用场景，帮助开发者实现绿色化部署，提升自动化脚本开发效率。
硬件即代码：用软件工程思维拆解F450无人机装配的模块化设计哲学
本文探讨了如何用软件工程思维重构DJI-F450无人机的模块化设计，将硬件装配提升为可编程系统。通过硬件抽象层、控制系统分层架构和扩展槽位设计模式，展示了无人机装配的模块化哲学，并结合实际案例说明如何优化性能与安全性。
STM32F103移植NuttX实战：从零搭建最小系统到串口Shell（附完整配置文件）
本文详细介绍了在STM32F103C8T6开发板上移植NuttX实时操作系统的完整过程，包括环境搭建、BSP配置、系统时钟初始化、驱动裁剪、编译烧录及串口Shell交互。通过实战指南帮助开发者快速掌握NuttX移植技巧，解决典型问题，适用于嵌入式系统开发。
如何快速获取SAMM、SMIC等主流微表情数据集？完整申请指南（附避坑技巧）
本文提供了获取SAMM、SMIC等主流微表情数据集的完整申请指南，包括申请前的准备工作、分步申请流程、协议谈判技巧以及数据使用与维护策略。特别针对高分辨率动态捕捉和多模态同步数据等热词，帮助研究者高效获取高质量数据集，避免常见陷阱。
2026年海能达嵌入式岗位面试题集（含参考答案）
本文总结了嵌入式系统开发中的常见面试题，涵盖项目经验、外设接口、存储系统、编程基础、操作系统等核心模块。主要内容包括：1）项目核心经历的硬件/软件架构及问题解决思路；2）SPI、I2C等接口协议的时序特点与应用要点；3）NAND Flash存储管理中的ECC校验原理和文件系统对接方案；4）USB设备枚举流程等关键知识点。这些题目针对嵌入式岗位的技术考察重点，既包含底层硬件接口细节，也涉及系统级设计与优化策略，全面考察开发者的嵌入式系统能力。
MCP+A2A：从实验室到生产环境的落地之旅
本文通过实际项目中的落地案例，展示了 MCP+A2A 协议结合使用的强大功能和广泛适用性。同时，我们也探讨了开发者社区的贡献、与其他技术的融合，以及未来技术趋势的展望。希望本文能够帮助读者更好地理解如何将这一架构从实验室推向生产环境，推动 AI 技术的实际应用。
VB摄像头监控
该VB模块可对摄像头捕获的画面进行录像等，可在创建的窗口进行控制
Outlook Express 6 (OE6) for Win7/Win8/Win10
英文版 Outlook Express6.0， 兼容Win7/Win10/WinXP，解压就可用。
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")
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。
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
1597
扫一扫
