Modeling Camera Image Formation Using a Feedforward Neural Network

Yongtae Do

Electronic Control Major, Division of Electronic & Electrical Engineering, Daegu University, 712-714, South Korea.

DOI: 10.4236/ojapps.2013.31B015                

Abstract

One fundamental problem in computer vision and image processing is modeling the image formation of a camera, i.e., mapping a point in three-dimensional space to its projected position on the camera’s image plane. If the relationship between the space and the image plane is assumed to be linear, the relationship can be expressed in terms of a transfor-mation matrix and the matrix is often identified by regression. In this paper, we show that the space-to-image relation-ship in a camera can be modeled by a simple neural network. Unlike most other cases employing neural networks, the structure of the network is optimized so as for each link between neurons to have a physical meaning. This makes it possible to effectively initialize link weights and quickly train the network.

Keywords

Camera Model; Camera Calibration; Image Formation; Neural Network

Conflicts of Interest

The authors declare no conflicts of interest.

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