psf的matlab代码-Localization:本土化
psf的matlab代码本土化
这是通过在Matlab（Matconvnet）和Python（Keras和TensorFlow）上进行深度学习而实现的三维本地化显微镜的实现。
该模型基于深度卷积神经网络（CNN），可从常规宽视野荧光显微镜捕获的单个2D图像中检索荧光团的3D位置。
具有挑战性的3D定位通过两个级联的CNN转换为多标签分类问题。
该存储库包括：
珠子/细胞/颗粒定位培训和测试的源代码。
模拟训练数据集的源代码。
斑马鱼血液移动的数据集通过宽视野荧光显微镜收集，用于细胞定位/跟踪。
在模拟/收集的数据集上进行训练和测试的示例。
该代码已记录并设计为易于扩展。
如果您在研究中使用它，请考虑引用该存储库（下面的bibtex）。
入门
斑马鱼或系统PSF的数据集可从Google云端硬盘获取。
对于Python
3.5.5用户，请同时安装和。
对于Matlab用户，请安装。
训练级联的CNN
train_network_A.py(.m)提供了一个训练第一个CNN（横向检测CNN）以确定每个贴片的中心横向位置是否存在衍射图样的示例。
train_network_B.py(.m)提供了
文件下载
资源详情
[{"title":"（ 66 个子文件 100KB ） psf的matlab代码-Localization:本土化","children":[{"title":"Localization-master","children":[{"title":"matlab","children":[{"title":"Cell_Localization","children":[{"title":"train_network_A.m <span style='color:#111;'> 4.39KB </span>","children":null,"spread":false},{"title":"train_network_B","children":[{"title":"my_getBatchWrapper_xyz.m <span style='color:#111;'> 3.96KB </span>","children":null,"spread":false},{"title":"my_sampleGen.m <span style='color:#111;'> 2.93KB </span>","children":null,"spread":false},{"title":"my_cnnInitializeModel_xyz.m <span style='color:#111;'> 9.01KB </span>","children":null,"spread":false},{"title":"my_setup_na.m <span style='color:#111;'> 1.69KB </span>","children":null,"spread":false},{"title":"my_depthPrediction_xyz.m <span style='color:#111;'> 2.58KB </span>","children":null,"spread":false}],"spread":true},{"title":"test_network.m <span style='color:#111;'> 7.41KB </span>","children":null,"spread":false},{"title":"train_network_B.m <span style='color:#111;'> 4.78KB </span>","children":null,"spread":false},{"title":"train_network_A","children":[{"title":"my_getBatchWrapper_xyz.m <span style='color:#111;'> 4.36KB </span>","children":null,"spread":false},{"title":"my_sampleGen.m <span style='color:#111;'> 2.53KB </span>","children":null,"spread":false},{"title":"my_cnnInitializeModel_xyz.m <span style='color:#111;'> 9.19KB </span>","children":null,"spread":false},{"title":"my_setup_na.m <span style='color:#111;'> 1.91KB </span>","children":null,"spread":false},{"title":"my_depthPrediction_xyz.m <span style='color:#111;'> 2.58KB </span>","children":null,"spread":false}],"spread":true},{"title":"test_network","children":[{"title":"my_fun_2_locationFit.m <span style='color:#111;'> 1.38KB </span>","children":null,"spread":false},{"title":"my_fun_1_evaluateSingle.m <span style='color:#111;'> 1.36KB </span>","children":null,"spread":false},{"title":"my_kalmanTracking.m <span style='color:#111;'> 6.24KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"SR_Localization","children":[{"title":"train_network_A.m <span style='color:#111;'> 3.09KB </span>","children":null,"spread":false},{"title":"train_network_B","children":[{"title":"my_getBatchWrapper_xyz.m <span style='color:#111;'> 1.63KB </span>","children":null,"spread":false},{"title":"my_cnnInitializeModel_xyz.m <span style='color:#111;'> 6.17KB </span>","children":null,"spread":false},{"title":"my_setup_sm.m <span style='color:#111;'> 2.10KB </span>","children":null,"spread":false},{"title":"my_prediction_xyz.m <span style='color:#111;'> 1.40KB </span>","children":null,"spread":false}],"spread":true},{"title":"test_network.m <span style='color:#111;'> 7.17KB </span>","children":null,"spread":false},{"title":"train_network_B.m <span style='color:#111;'> 4.33KB </span>","children":null,"spread":false},{"title":"train_network_A","children":[{"title":"my_getBatchWrapper_xyz.m <span style='color:#111;'> 1.25KB </span>","children":null,"spread":false},{"title":"my_cnnInitializeModel_xyz.m <span style='color:#111;'> 5.09KB </span>","children":null,"spread":false},{"title":"my_setup_sm.m <span style='color:#111;'> 2.18KB </span>","children":null,"spread":false},{"title":"my_prediction_xyz.m <span style='color:#111;'> 1.21KB </span>","children":null,"spread":false}],"spread":true},{"title":"test_network","children":[{"title":"my_evalRes.m <span style='color:#111;'> 2.14KB </span>","children":null,"spread":false},{"title":"my_fun_1_evaluateSingle.m <span style='color:#111;'> 1.21KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"my_util","children":[{"title":"convnfft.m <span style='color:#111;'> 6.46KB </span>","children":null,"spread":false},{"title":"my_simSplinePSF.m <span style='color:#111;'> 2.73KB </span>","children":null,"spread":false},{"title":"my_readKernel.m <span style='color:#111;'> 896B </span>","children":null,"spread":false},{"title":"my_mkdir.m <span style='color:#111;'> 190B </span>","children":null,"spread":false},{"title":"computeDelta3Dj_v2.m <span style='color:#111;'> 1.14KB </span>","children":null,"spread":false},{"title":"progress.m <span style='color:#111;'> 220B </span>","children":null,"spread":false},{"title":"fAt3Dj_v2.m <span style='color:#111;'> 251B </span>","children":null,"spread":false},{"title":"my_cut.m <span style='color:#111;'> 159B </span>","children":null,"spread":false},{"title":"my_dir.m <span style='color:#111;'> 1.12KB </span>","children":null,"spread":false},{"title":"conv2fft.m <span style='color:#111;'> 1.51KB </span>","children":null,"spread":false}],"spread":true},{"title":"Bead_Localization","children":[{"title":"train_network_A.m <span style='color:#111;'> 3.91KB </span>","children":null,"spread":false},{"title":"train_network_B","children":[{"title":"my_getBatchWrapper_xyz.m <span style='color:#111;'> 3.96KB </span>","children":null,"spread":false},{"title":"my_sampleGen.m <span style='color:#111;'> 2.93KB </span>","children":null,"spread":false},{"title":"my_cnnInitializeModel_xyz.m <span style='color:#111;'> 9.01KB </span>","children":null,"spread":false},{"title":"my_setup_na.m <span style='color:#111;'> 1.69KB </span>","children":null,"spread":false},{"title":"my_depthPrediction_xyz.m <span style='color:#111;'> 2.58KB </span>","children":null,"spread":false}],"spread":true},{"title":"test_network.m <span style='color:#111;'> 4.98KB </span>","children":null,"spread":false},{"title":"train_network_B.m <span style='color:#111;'> 4.03KB </span>","children":null,"spread":false},{"title":"train_network_A","children":[{"title":"my_getBatchWrapper_xyz.m <span style='color:#111;'> 4.36KB </span>","children":null,"spread":false},{"title":"my_sampleGen.m <span style='color:#111;'> 2.53KB </span>","children":null,"spread":false},{"title":"my_cnnInitializeModel_xyz.m <span style='color:#111;'> 9.19KB </span>","children":null,"spread":false},{"title":"my_setup_na.m <span style='color:#111;'> 1.91KB </span>","children":null,"spread":false},{"title":"my_depthPrediction_xyz.m <span style='color:#111;'> 2.58KB </span>","children":null,"spread":false}],"spread":true},{"title":"test_network","children":[{"title":"my_fun_2_locationFit.m <span style='color:#111;'> 1.38KB </span>","children":null,"spread":false},{"title":"my_evalRes.m <span style='color:#111;'> 2.16KB </span>","children":null,"spread":false},{"title":"my_fun_1_evaluateSingle.m <span style='color:#111;'> 1.36KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true},{"title":"README.md <span style='color:#111;'> 2.62KB </span>","children":null,"spread":false},{"title":"python","children":[{"title":"SR_Localization","children":[{"title":"train_network_B.py <span style='color:#111;'> 7.51KB </span>","children":null,"spread":false},{"title":"test_network.py <span style='color:#111;'> 9.69KB </span>","children":null,"spread":false},{"title":"parameters.py <span style='color:#111;'> 3.07KB </span>","children":null,"spread":false},{"title":"DataGeneration.py <span style='color:#111;'> 8.77KB </span>","children":null,"spread":false},{"title":"train_network_A.py <span style='color:#111;'> 6.47KB </span>","children":null,"spread":false}],"spread":true},{"title":"Bead_Localization","children":[{"title":"train_network_B.py <span style='color:#111;'> 5.48KB </span>","children":null,"spread":false},{"title":"test_network.py <span style='color:#111;'> 7.79KB </span>","children":null,"spread":false},{"title":"parameters.py <span style='color:#111;'> 1.76KB </span>","children":null,"spread":false},{"title":"DataGeneration.py <span style='color:#111;'> 12.03KB </span>","children":null,"spread":false},{"title":"train_network_A.py <span style='color:#111;'> 5.46KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}],"spread":true}]
评论信息
其他资源
- 多径和多普勒频移的原理
- DWM1000官方资料+例程+中文数据手册
- 微电子电路第五版习题答案 Microelectronic Circuits Solution
- MATLAB细胞神经网络（CNN）图像处理
- 液晶字模软件zimo221
- SSM简单的人事管理系统
- 单相boost电路平均电流模式PFC仿真研究以及参数计算-Single_Phase_PFC.slx
- Echarts 全国各市地图js文件
- 基于SQL Server的公司进销存管理系统（vb+mssql）源码
- fdtd中文教程
- 基于C# MVC开发的WEB API通用框架 附带 微信 支付宝支付集成
- 智能arduino小车源程序
- FANUC数据采集
- 神州数码java笔试题
- 马尔可夫过程在信源编码
- 交叉25码（ITF25）编码程序代码
- VRTK_HKWorkshop_Advanced.pdf
- ffmpeg-ios-master.zip
- PC桌面运维“生态圈”运营策划-信息化项目策划
- Spring-generator图形界面Spring代码生成器
- 八位二进制加法（proteus仿真电路）
- Visual AssistX 10.9 2302版本通用和谐方案
免责申明
【只为小站】的资源来自网友分享，仅供学习研究，请务必在下载后24小时内给予删除，不得用于其他任何用途，否则后果自负。基于互联网的特殊性，【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查；无论 【只为小站】 经营者是否已进行审查，用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明
本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明