Imperial College London

DrTae-KyunKim

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6317tk.kim Website

 
 
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Location

 

1017Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Tsiotsios:2016:10.1016/j.cviu.2016.03.002,
author = {Tsiotsios, C and Kim, TK and Davison, AJ and Narasimhan, SG},
doi = {10.1016/j.cviu.2016.03.002},
journal = {Computer Vision and Image Understanding},
pages = {126--138},
title = {Model effectiveness prediction and system adaptation for photometric stereo in murky water},
url = {http://dx.doi.org/10.1016/j.cviu.2016.03.002},
volume = {150},
year = {2016}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - In murky water, the light interaction with the medium particles results in a complex image formation model that is hard to use effectively with a shape estimation framework like Photometric Stereo. All previous approaches have resorted to necessary model simplifications that were though used arbitrarily, without describing how their validity can be estimated in an unknown underwater situation. In this work, we evaluate the effectiveness of such simplified models and we show that this varies strongly with the imaging conditions. For this reason, we propose a novel framework that can predict the effectiveness of a photometric model when the scene is unknown. To achieve this we use a dynamic lighting framework where a robotic platform is able to probe the scene with varying light positions, and the respective change in estimated surface normals serves as a faithful proxy of the true reconstruction error. This creates important benefits over traditional Photometric Stereo frameworks, as our system can adapt some critical factors to an underwater scenario, such as the camera-scene distance and the light position or the photometric model, in order to minimize the reconstruction error. Our work is evaluated through both numerical simulations and real experiments for different distances, underwater visibilities and light source baselines.
AU - Tsiotsios,C
AU - Kim,TK
AU - Davison,AJ
AU - Narasimhan,SG
DO - 10.1016/j.cviu.2016.03.002
EP - 138
PY - 2016///
SN - 1090-235X
SP - 126
TI - Model effectiveness prediction and system adaptation for photometric stereo in murky water
T2 - Computer Vision and Image Understanding
UR - http://dx.doi.org/10.1016/j.cviu.2016.03.002
VL - 150
ER -