Parameterized Synthetic Image Data Set for Fisheye Lens
This provides a scalable solution for researchers and practitioners in computer vision working with fisheye lenses, though it is incremental as it extends existing synthetic data methods to a specific domain.
The authors tackled the problem of limited training data for deep neural networks in fisheye image parameter extraction by proposing a parameterized synthetic image dataset, which effectively boosts diversity and avoids scale limitations, as proven by testing with a fisheye camera.
Based on different projection geometry, a fisheye image can be presented as a parameterized non-rectilinear image. Deep neural networks(DNN) is one of the solutions to extract parameters for fisheye image feature description. However, a large number of images are required for training a reasonable prediction model for DNN. In this paper, we propose to extend the scale of the training dataset using parameterized synthetic images. It effectively boosts the diversity of images and avoids the data scale limitation. To simulate different viewing angles and distances, we adopt controllable parameterized projection processes on transformation. The reliability of the proposed method is proved by testing images captured by our fisheye camera. The synthetic dataset is the first dataset that is able to extend to a big scale labeled fisheye image dataset. It is accessible via: http://www2.leuphana.de/misl/fisheye-data-set/.