Learning image distortion using a GMDH network

Authors:
Yongtae Do;Myounghwan Kim
Affiliations:
School of Electronic Engineering, Daegu University, Gyeongsan-City, Gyeongbuk, South Korea;School of Electronic Engineering, Daegu University, Gyeongsan-City, Gyeongbuk, South Korea
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

Using the Group Method of Data Handling (GMDH) a polynomial network is designed in this paper for learning the nonlinear image distortion of a camera. The GMDH network designed can effectively learn image distortion in various camera systems of different optical features unlike most existing techniques that assume a physical model explicitly. Compared to multilayer perceptrons (MLPs), which are popularly used to learn a nonlinear relation without modeling, a GMDH network is self-organizing and its learning is faster. We prove the advantages of the proposed technique with various simulated data sets and in a real experiment.