图像配准定位


项目名称:智能笔                                                           

项目语言:C/C++


功能简介:

摄像头拍摄目标,从中截取一帧,通过Surf算法提取图片特征,提取完特征后与事先准备好的特征库进行比对;由于特征库比较大,因此需要一个有效的数据管理结构,这里使用的是KD树存储特征;提取出的特征与特征库匹配时为了保证效率使用BBF算法搜索特征树,然后通过ķ近邻进行匹配,但是由于特征太多,直接匹配正确率很低,这里可以通过方差来修正,但是方差浪费时间,可以通过另一种方法修正,这里涉及保密不做介绍。匹配完成后再次使用Ransac算法进行匹配点筛选错匹配点,同时获得一个基础矩阵,最后通过仿射变换把图片从模板中标记出来。


使用算法:

Algorithm Purpose
Surf/Sift特征提取
KdTree存储特征
Bbf数据查找
Knn特征匹配
Ransac特征筛选
Jacobi 计算矩阵特征值

程序流程:

程序执行过程

结果展示:

1.目标图像

左图是原图,右图是剪切图(摄像头图像)

2.源图像

源1 ,源2, 源3

效果图:

匹配定位效果图

本文总结于网络文章,加入了个人理解,仅用于个人学习研究,不得用于其他用途,如涉及版权问题,请联系邮箱513403849@qq.com

空洞卷积及语义分割的改进方法

文档链接: 空洞卷积及语义分割改进方法

github

本文总结于网络文章,加入了个人理解,仅用于个人学习研究,不得用于其他用途,如涉及版权问题,请联系邮箱513403849@qq.com

python 第三方库

下载地址
PyPI地址:https://pypi.org/project/boto/#files(修改boto为自己的软件包名)
阿里云:http://mirrors.aliyun.com/pypi/simple/
中国科技大学:https://pypi.mirrors.ustc.edu.cn/simple/
豆瓣(douban):http://pypi.douban.com/simple/
清华大学:https://pypi.tuna.tsinghua.edu.cn/simple/
中国科学技术大学:http://pypi.mirrors.ustc.edu.cn/simple/

如果想配置成默认的源,方法如下:
需要创建或修改配置文件(一般都是创建),
linux的文件在~/.pip/pip.conf,
windows在%HOMEPATH%\pip\pip.ini),
修改内容为:
[global]
index-url = http://pypi.douban.com/simple
[install]
trusted-host=pypi.douban.com
这样在使用pip来安装时,会默认调用该镜像。

Brain Tumors

项目名称:脑肿瘤分割
编程语言:Keras/Python


Introduction:

Task 1: Segmentation of gliomas in pre-operative MRI scans.
       The participants are called to address this task by using the provided clinically-acquired training data to develop their method and produce segmentation labels of the different glioma sub-regions. The sub-regions considered for evaluation are: 1) the “enhancing tumor” (ET), 2) the “tumor core” (TC), and 3) the “whole tumor” (WT) [see figure below]. The ET is described by areas that show hyper-intensity in T1Gd when compared to T1, but also when compared to “healthy” white matter in T1Gd. The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (fluid-filled) and the non-enhancing (solid) parts of the tumor. The appearance of the necrotic (NCR) and the non-enhancing (NET) tumor core is typically hypo-intense in T1-Gd when compared to T1. The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edema (ED), which is typically depicted by hyper-intense signal in FLAIR.
       The labels in the provided data are: 1 for NCR & NET2 for ED4 for ET, and 0 for everything else.
       The participants are called to upload their segmentation labels into CBICA’s Image Processing Portal for evaluation.

Task 2: Prediction of patient overall survival (OS) from pre-operative scans.
       Once the participants produce their segmentation labels in the pre-operative scans, they will be called to use these labels in combination with the provided multimodal MRI data to extract imaging/radiomic features that they consider appropriate, and analyze them through machine learning algorithms, in an attempt to predict patient OS. The participants do not need to be limited to volumetric parameters, but can also consider intensity, morphologic, histogram-based, and textural features, as well as spatial information, and glioma diffusion properties extracted from glioma growth models.
       Note that participants are expected to provide predicted survival status only for subjects with resection status of GTR (i.e., Gross Total Resection).
      The participants are called to upload a .csv file with the subject ids and the predicted survival values into CBICA’s Image Processing Portal for evaluation.

Feel free to send any communication related to the BraTS challenge to brats2018@cbica.upenn.edu


Process:


Model:

paper:Multi-level Activation for Segmentation of Hierarchically-nested Classes on 3D-Unet


Tricks:

MethodsName
NormalizationBN / LN / IN / SN
DroupoutS dropout / dropblock / Targeted dropout
Reduce dim1*1 kernel
AugmentSTN / Deformable ConvNets
ContextSPP / ParseNet / PSP / HDC / ASPP / FPA
Updeconvolution / bilinear / DUC / GAU
Crfdense crf
Domainconnected domain
Early stopyes
Lossdice loss / jaccard loss / focal loss
Regularizationl1_l2 / W constraint
ActivationReLU / PreLU / LeakyReLU / ELU

结果:

配置:显卡Nvidia 1060, 图片使用112*112*112
训练:Epoch 175/500 loss: -0.7303 – jaccard: 0.6066 – val_loss: -0.6615 – val_jaccard: 0.5421
Scores:
1.WT:0.8886     
2.TC:0.8049     
3.ET:0.7772
learderboard 2019 :leardboard

Continue reading Brain Tumors

去除pycharm的warning提示类的波浪线

  • 找到setting选项中的Editor的Inspections,打开Python选项夹将其中所有的PEP8选项的对勾去除(用于去除def函数命名的检查和import时from的提示);
  • 找到setting选项中的Editor的Inspections,去除spelling中的typo选项的对勾。(用于出去部分变量命名的问题);
  • 找到setting选项中的Editor的Color Scheme,在general选项点击Errors and Warnings,将其中的Weak Warning的对勾去除。(用于去除除了2之外其他变量命名的问题)

通过以上三项能把pycharm中的警告类的波浪线去除,而不会将真正的错误提醒去除,终于看上去舒服了吧。

作者:CristianoCN
来源:CSDN
原文:https://blog.csdn.net/xdsjlpld123/article/details/79805497