From f56f5330789e99ad426812500fc2b0c1defa2596 Mon Sep 17 00:00:00 2001 From: WookCheolShin Date: Wed, 18 Dec 2019 00:29:22 +0900 Subject: [PATCH] initial commit --- README.md | 81 ++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 80 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 752c4e9..25fa8ed 100644 --- a/README.md +++ b/README.md @@ -1 +1,80 @@ -# Noise-AwareCameraExposureControl \ No newline at end of file +# Camera Exposure Control for Robust Robot Vision with Noise-Aware Image Quality Assessment + +In this paper, we propose a noise-aware exposure control algorithm for robust robot vision. Our method aims to capture best-exposed images, which can boost the performance of various computer vision and robotics tasks. Our metric consists of a combination of image gradient, entropy, and noise metrics. The synergy of these measures allows the preservation of sharp edges and rich texture in the image while maintaining a low noise level. +Using this novel metric, we propose a real-time and fully automatic exposure and gain control technique based on the Nelder-Mead method. + +## Overview +![](media/Teaser.png) +

+ + +

+ +[**[Full paper]**](https://arxiv.org/abs/1907.12646) [**[YouTube]**](https://www.youtube.com/watch?v=9ILFITEwNX0) + +## Dataset +

+ +

+ +In this paper, we provide a unique dataset developed specifically to compare exposure control algorithms. +The composition of this dataset is as follows. +- **HW setup:** a stereo camera system with 20 cm baseline acquiring synchronized 1600 x 1200 px images. +- **# of scene:** Total 25 scene (10 indoor, 15 outdoor) +- **# of image:** Each scene consist of 550 x 2 images + - Outdoor Environment + - Exposure time : [0.1 - 7.45 ms] with 0.15 ms interval + - Gain : [0 - 20]dB with 2dB interval + - Indoor Environment + - Exposure time : [4 - 67 ms] with 3 ms interval + - Gain : [0 - 24]dB with 1dB interval +- **# of object class:** 13 Object Class + - Person, Bicycle, Car, Firehydrant, Backpack, Sports ball, Chair, Mouse, Keyboard, Cellphone, Book, Scissors, and TV. + - However, some objects appear very rarely, we need to acquire more dataset. +- **Download link:** https://drive.google.com/file/d/1DUeByL_ADzGyRpqc0iXA-DxZwLOd5Q-U/view?usp=sharing + + +## Quick Start: Demo Script +### 1. Download the datset +```bash +clone this repositroy +donwload datset from [here](https://drive.google.com/file/d/1DUeByL_ADzGyRpqc0iXA-DxZwLOd5Q-U/view?usp=sharing) +unzip "DataSet_AE.zip" +``` + +### 2. Run MetricEvaluation.m +```bash +run "Run_MetricEvaluation.m" +``` +Then, this matlab code create and save a landscape of the proposed image quaility assessment metric for each dataset. + +Note that, we have cleaned and optimized the code for better readability. However, the result could be slightly different from the result reported in the paper. + +The original results, including related works, can be available [here](https://drive.google.com/open?id=1arj7DjiY6gHdqbuLdzC1N4B2EANXYYTv). + +If you want use original results, unzip this file into "Result_Exp" folder. + +### 3. Run NMbasedControl.m +```bash +run "NMbasedControl.m" +``` +This matlab code runs the paper version of the Nelder-Mead optimization based control algorithm for each dataset. + + +### 4. Run FeatureMatching.m +```bash +run "FeatureMatching.m" +``` +This matlab code conducts a experiment about a feature extraction & matching. +To do this experiment, you need to install OpenCV on matlab by following [this link](https://github.com/kyamagu/mexopencv/wiki/Installation-%28Windows%2C-MATLAB%2C-OpenCV-3%29). + +## Citation +Please cite the following paper if you use our work or parts of this code in your own work. +``` +@article{shin2019camera, + title={Camera exposure control for robust robot vision with noise-aware image quality assessment}, + author={Shin, Ukcheol and Park, Jinsun and Shim, Gyumin and Rameau, Francois and Kweon, In So}, + journal={arXiv preprint arXiv:1907.12646}, + year={2019} +} +```