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dc.contributor.authorChen, Long
dc.contributor.authorTang, Wen
dc.contributor.authorWan, Tao Ruan
dc.contributor.authorJohn, Nigel W.
dc.date.accessioned2020-01-30T09:23:00Z
dc.date.available2020-01-30T09:23:00Z
dc.identifier.citationChen, L., Tang, W., Wan, T. R., & John, N. W., (2019). Self-supervised monocular image depth learning and confidence estimation. Neurocomputing, 381, 272-281.en_US
dc.identifier.urihttp://hdl.handle.net/10034/623135
dc.description.abstractWe present a novel self-supervised framework for monocular image depth learning and confidence estimation. Our framework reduces the amount of ground truth annotation data required for training Convolutional Neural Networks (CNNs), which is often a challenging problem for the fast deployment of CNNs in many computer vision tasks. Our DepthNet adopts a novel fully differential patch-based cost function through the Zero-Mean Normalized Cross Correlation (ZNCC) to take multi-scale patches as matching and learning strategies. This approach greatly increases the accuracy and robustness of the depth learning. Whilst the proposed patch-based cost function naturally provides a 0-to-1 confidence, it is then used to self-supervise the training of a parallel network for confidence map learning and estimation by exploiting the fact that ZNCC is a normalized measure of similarity which can be approximated as the confidence of the depth estimation. Therefore, the proposed corresponding confidence map learning and estimation operate in a self-supervised manner and is a parallel network to the DepthNet. Evaluation on the KITTI depth prediction evaluation dataset and Make3D dataset show that our method outperforms the state-of-the-art results.en_US
dc.publisherElsevieren_US
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S0925231219316388en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.subjectMonocular depth estimationen_US
dc.subjectDeep convolutional neural networksen_US
dc.subjectConfidence mapen_US
dc.titleSelf-supervised monocular image depth learning and confidence estimationen_US
dc.typeArticleen_US
dc.contributor.departmentBournemouth University; University of Bradford; University of Chesteren_US
dc.identifier.journalNeurocomputingen_US
or.grant.openaccessYesen_US
rioxxterms.funderunfundeden_US
rioxxterms.identifier.projectunfundeden_US
rioxxterms.versionAMen_US
rioxxterms.versionofrecordhttps://doi.org/10.1016/j.neucom.2019.11.038en_US
rioxxterms.licenseref.startdate2020-12-04
rioxxterms.publicationdate2019-12-04
dc.dateAccepted2019-11-23
dc.date.deposited2020-01-30en_US
dc.indentifier.issn0925-2312en_US


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