TY - CHAP

T1 - Parameterized Synthetic Image Data Set for Fisheye Lens

AU - Zhen, Chen

AU - Georgiadis, Anthimos

N1 - Conference code: 5

PY - 2018/7/2

Y1 - 2018/7/2

N2 - 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 for extracting the parameters for fisheye image feature expression. However, a number of images are required for training a reasonable prediction model for DNN. In this paper, we propose to extend the scale of the dataset using parameterized synthetic images which effectively boost the diversity of samples and avoid the limitation on the scale. To simulate different viewing angles and distances, we adopt controllable parameterized projection processes on transformation. The reliability of the proposed method is tested with the image captured by a fisheye camera. The synthetic fisheye image dataset is the first dataset that is developed by existing labeled perspective images. It is accessible via: http://www2.leuphana.de/misl/fisheye-data-set/.

AB - 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 for extracting the parameters for fisheye image feature expression. However, a number of images are required for training a reasonable prediction model for DNN. In this paper, we propose to extend the scale of the dataset using parameterized synthetic images which effectively boost the diversity of samples and avoid the limitation on the scale. To simulate different viewing angles and distances, we adopt controllable parameterized projection processes on transformation. The reliability of the proposed method is tested with the image captured by a fisheye camera. The synthetic fisheye image dataset is the first dataset that is developed by existing labeled perspective images. It is accessible via: http://www2.leuphana.de/misl/fisheye-data-set/.

KW - Fisheye lens

KW - Image processing

KW - Neural network

KW - Synthetic data set

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85062108273&partnerID=8YFLogxK

U2 - 10.1109/ICISCE.2018.00084

DO - 10.1109/ICISCE.2018.00084

M3 - Article in conference proceedings

AN - SCOPUS:85062108273

SP - 370

EP - 374

BT - Proceedings - 2018 5th International Conference on Information Science and Control Engineering, ICISCE 2018

A2 - Cheng, Y

A2 - Li, S.

A2 - Dai, Y.

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Conference on Information Science and Control Engineering - ICISCE 2018

Y2 - 20 July 2018 through 22 July 2018

ER -