@inproceedings{d78baa5ae0374e1685ce36bcf00bda28,
title = "ConfiConv: A Confidence Measure for Disparity Estimation Based on Deep Learning",
abstract = "In this paper, a confidence measure for disparity estimation is proposed to encode the degree of uncertainty of each point in disparity map. Based on Convolutional Neural Network (CNN), a network is set up which is named as Confidence Convolutional neural network (ConfiConv). Compared with four different Confidence Measures (CMs) in Middlebury 2014 dataset using two stereo vision algorithms, it is shown that the average Area Under Curve (AUC) values of ConfiConv are better than other measures. And ConfiConv is also tested in another dataset which contains multiple movie clips.",
keywords = "Confidence Measure, Convolutional Neural Network, Disparity",
author = "Hao Wang and Yaping Dai and Kaizheng Chen and Zhiyang Jia and Yongkang Nie",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 31st Chinese Control and Decision Conference, CCDC 2019 ; Conference date: 03-06-2019 Through 05-06-2019",
year = "2019",
month = jun,
doi = "10.1109/CCDC.2019.8832630",
language = "English",
series = "Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1360--1364",
booktitle = "Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019",
address = "United States",
}