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.
Original language | English |
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Title of host publication | Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1360-1364 |
Number of pages | 5 |
ISBN (Electronic) | 9781728101057 |
DOIs | |
Publication status | Published - Jun 2019 |
Event | 31st Chinese Control and Decision Conference, CCDC 2019 - Nanchang, China Duration: 3 Jun 2019 → 5 Jun 2019 |
Publication series
Name | Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019 |
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Conference
Conference | 31st Chinese Control and Decision Conference, CCDC 2019 |
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Country/Territory | China |
City | Nanchang |
Period | 3/06/19 → 5/06/19 |
Keywords
- Confidence Measure
- Convolutional Neural Network
- Disparity
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Wang, H., Dai, Y., Chen, K., Jia, Z., & Nie, Y. (2019). ConfiConv: A Confidence Measure for Disparity Estimation Based on Deep Learning. In Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019 (pp. 1360-1364). Article 8832630 (Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC.2019.8832630