Assembling convolution neural networks for automatic viewing transformation
journal contributionposted on 2019-09-27, 12:27 authored by Haibin Cai, Lei Jiang, Bangli Liu, Yiqi Deng, Qinggang MengQinggang Meng
Images taken under different camera poses are rotated or distorted, which leads to poor perception experiences. This paper proposes a new framework to automatically transform the images to the conformable view setting by assembling different convolution neural networks. Specifically, a referential 3D ground plane is firstly derived from the RGB image and a novel projection mapping algorithm is developed to achieve automatic viewing transformation. Extensive experimental results demonstrate that the proposed method outperforms the state-ofthe-art vanishing points based methods by a large margin in terms of accuracy and robustness.
YOBAN project (Newton Fund/Innovate UK, 102871)
SukeIntel Co., Ltd
- Computer Science
Published inIEEE Transactions on Industrial Informatics
Pages587 - 594
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
- AM (Accepted Manuscript)
Rights holder© IEEE
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