Cai, Haibin Jiang, Lei Liu, Bangli Deng, Yiqi Meng, Qinggang Assembling convolution neural networks for automatic viewing transformation 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. Electrical & Electronic Engineering;Information and Computing Sciences;Engineering;Technology;Automatic viewing transform;Convolution neural networks;Deep learning 2019-09-27
    https://repository.lboro.ac.uk/articles/journal_contribution/Assembling_convolution_neural_networks_for_automatic_viewing_transformation/9912980