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richardboo's Projects

munit

Multimodal Unsupervised Image-to-Image Translation

nlp-loss-pytorch

Implementation of some unbalanced loss like focal_loss, dice_loss, DSC Loss, GHM Loss et.al

onecut

One dimensional cut problem solver (Italian)

opence

Contrast Enhancement Techniques for low-light images

opencl-book-samples

Automatically exported from code.google.com/p/opencl-book-samples

openface

OpenFace – a state-of-the art tool intended for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation.

opengl-refpages

OpenGL and OpenGL ES reference page sources, and generated HTML used as backing store for khronos.org

opengles-book-samples

Automatically exported from code.google.com/p/opengles-book-samples

opengles2-book

OpenGL ES 2.0 Programming Guide Sample Code

opengles3-book

OpenGL ES 3.0 Programming Guide Sample Code

pedestrian_detection

通过HOG+SVM训练进行行人检测，行人数据库使用INRIAPerson，程序基于OpenCV实现

photoflow

A fully non-destructive photo retouching program providing a complete layer-based workflow including RAW image development.

pm-ocl

GPU Powered Perona – Malik Anisotropic Filter

poisson-image-editing

Poisson image editing for seamless cloning and other operations

poissonimageediting

poisson image editing , Image Collage，图像编辑，图像拼贴

pspnet

Pyramid Scene Parsing Network, CVPR2017.

pwc

Papers with code. Sorted by stars. Updated weekly.

pytorch-network-slimming

A package to make do Network Slimming easier

pytorch-template

My PyTorch project template (for Kaggle and research)

rcnn

R-CNN: Regions with Convolutional Neural Network Features

retouch

An OpenGL application for editing and retouching images using depth-maps in 2.5D

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

rrc_detection

Accurate Single Stage Detector Using Recurrent Rolling Convolution

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