@inproceedings{02e1265ba0f340abab0092e6cf351c25,
title = "FishEyeRecNet: A multi-context collaborative deep network for fisheye image rectification",
abstract = "Images captured by fisheye lenses violate the pinhole camera assumption and suffer from distortions. Rectification of fisheye images is therefore a crucial preprocessing step for many computer vision applications. In this paper, we propose an end-to-end multi-context collaborative deep network for removing distortions from single fisheye images. In contrast to conventional approaches, which focus on extracting hand-crafted features from input images, our method learns high-level semantics and low-level appearance features simultaneously to estimate the distortion parameters. To facilitate training, we construct a synthesized dataset that covers various scenes and distortion parameter settings. Experiments on both synthesized and real-world datasets show that the proposed model significantly outperforms current state of the art methods. Our code and synthesized dataset will be made publicly available.",
keywords = "Collaborative deep network, Distortion parameter estimation, Fisheye image rectification",
author = "Xiaoqing Yin and Xinchao Wang and Jun Yu and Maojun Zhang and Pascal Fua and Dacheng Tao",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",
year = "2018",
doi = "10.1007/978-3-030-01249-6\_29",
language = "英语",
isbn = "9783030012489",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "475--490",
editor = "Martial Hebert and Vittorio Ferrari and Cristian Sminchisescu and Yair Weiss",
booktitle = "Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings",
address = "德国",
}