hnexplorer Goto Github PK

2019-ncov

This repo holds the code for crawling the latest news on the pneumonia virus from Clove doctor's website

berkeley-stat-157

Homepage for STAT 157 at UC Berkeley

circle-detection

Circle detection/circle detector by arc-support line segments. The source code for IEEE ICIP paper.

cnn-explainer

Learning Convolutional Neural Networks with Interactive Visualization. https://poloclub.github.io/cnn-explainer/

coronavirus-epidemic-2019-ncov

👩🏻‍⚕️2019-nCoV estimation and forecast using statistical model; 新型冠状病毒武汉肺炎统计模型预测

d2l-zh

《动手学深度学习》：面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。

deep-learning-with-tensorflow-book

深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.

deep_sort

Simple Online Realtime Tracking with a Deep Association Metric

dive-into-dl-pytorch

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

document-image-skew-adjustment

文档图像倾斜角检测及校正

dummy-robot

我的超迷你机械臂机器人项目。

dxy-2019-ncov-crawler

2019新型冠状病毒疫情实时爬虫

easydl-courses

EasyDL公开课示例代码

exemplar-based-image-inpainting

exemplarsvm

Ensemble of Exemplar-SVMs for Object Detection and Beyond

fanqiang

翻墙-科学上网

high-quality-ellipse-detection

A high-quality ellipse detector based on arc-support line segments which can both accurately and efficiently detect ellipses in images.

image-inpainting

A paper list of image inpainting

intro_ds_wy_course

《精通数据科学：从线性回归到深度学习》视频课程的配套资料

lrslibrary

Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos

ncov-2019-data-science

proximal-dehaze-net-cpu

CPU based Matlab code for ECCV paper "Proximal Dehaze-Net: A Prior Learning-Based Deep Network for Single Image Dehazing".

pythondatasciencehandbook

Python Data Science Handbook: full text in Jupyter Notebooks

recursive-deep-residual-learning-for-single-image-dehazing-drl

Recursive Deep Residual Learning for Single Image Dehazing (DRL)

robotics-course-project

Haze can cause poor visibility and loss of contrast in images and videos. In this article, we study the dehazing problem which can improve visibility and thus help in many computer vision applications. An extensive comparison of state of the art single image dehazing methods is done. One simple contrast enhancement method is used for dehazing. Structure- texture decomposition has been used in conjunction with this enhancement method to improve its performance in presence of synthetic noise. Methods which use a haze formation model and attempt at solving an ill-posed problem using computer vision priors are also investigated. The two priors studied are dark channel prior and the non-local prior. Both qualitative and quantitative comparisons for atmospheric and underwater images on all three methods provide a conclusive idea of which dehazing method performs better. All this knowledge has been extended to video dehazing. A video dehazing method which uses the spatial and temporal information in a video is studied in depth. An improved version of video dehazing is proposed in this article, which uses the spatial-temporal information fusion framework but does not suffer from some of its limitations. The new video dehazing method is shown to produce better results on test videos