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README.md

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-# Noise-AwareCameraExposureControl
+# 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)
+<p align="center">
+  <img src="media/NMcontrol.gif" width="400" />
+  <img src="media/NMcontrol2.gif" width="400" />
+</p>
+
+[**[Full paper]**](https://arxiv.org/abs/1907.12646) [**[YouTube]**](https://www.youtube.com/watch?v=9ILFITEwNX0)
+
+## Dataset
+<p align="center">
+  <img src="media/Dataset.png" width="600" />
+</p>
+
+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}
+}
+```