%0 Journal Article %A Cai, Haibin %A Jiang, Lei %A Liu, Bangli %A Deng, Yiqi %A Meng, Qinggang %D 2019 %T Assembling convolution neural networks for automatic viewing transformation %U https://repository.lboro.ac.uk/articles/journal_contribution/Assembling_convolution_neural_networks_for_automatic_viewing_transformation/9912980 %2 https://ndownloader.figshare.com/files/17822021 %K Electrical & Electronic Engineering %K Information and Computing Sciences %K Engineering %K Technology %K Automatic viewing transform %K Convolution neural networks %K Deep learning %X 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. %I Loughborough University