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 article proposes a new framework to automatically transform the images to the conformable view setting by assembling different convolution neural networks. Specifically, a referential three-dimensional ground plane is first derived from the color 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-of-the-art vanishing points based methods by a large margin in terms of accuracy and robustness.