@inproceedings{ebf29dbcf7b2472ca8a91f2ade3e7094,
title = "Light field depth from multi-scale particle filtering",
abstract = "Rich information could be extracted from the high dimensional light field (LF) data, and one of the most fundamental output is scene depth. State-of-the-art depth calculation methods produce noisy calculations especially over texture-less regions. Based on Super-pixel segmentation, we propose to incorporate multi-level disparity information into a Bayesian Particle Filtering framework. Each pixels' individual as well as regional information are involved to give Maximum A Posteriori (MAP) predictions based on our proposed statistical model. The method can produce equivalent or better scene depth interpolation results than some of the state-of-the art methods, with possible potential in image processing applications such as scene alignment and stablization.",
keywords = "Bayesian, Depth Interpolation, Light Field, Particle Filter",
author = "Jie Chen and Chau, {Lap Pui} and He Li",
note = "Publisher Copyright: {\textcopyright} 2016 Asia Pacific Signal and Information Processing Association.; 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 ; Conference date: 13-12-2016 Through 16-12-2016",
year = "2017",
month = jan,
day = "17",
doi = "10.1109/APSIPA.2016.7820906",
language = "English",
series = "2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016",
}