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Recognition of Blurred Images Using Multilayer Neural Network Based on Multi-valued Neurons

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3 Author(s)
Igor Aizenberg ; Comput. Intell. Lab., Texas A&M Univ. - Texarkana, Texarkana, TX, USA ; Shane Alexander ; Jacob Jackson

In this paper, we consider a problem of blurred image recognition using a multilayer neural network based on multi-valued neurons (MLMVN). Recognition of blurred images is a challenging problem because it is difficult or even impossible to find any relevant space of features for solving this problem in the spatial domain. The first crucial point of our approach is the use of the frequency domain as a feature space. Since Fourier phase spectrum of a blurred image remains almost unaffected, at least in the low frequency part, it is possible to use phases corresponding to the lowest frequencies as features for recognition. To preserve the physical nature of phase, it is very important to use a machine learning tool for its analysis that treats the phase properly. MLMVN is based on multi-valued neurons whose inputs and output are located on the unit circle and are determined exactly by phase. This approach makes it possible to recognize even heavily blurred images. Our solution works even for images so degraded they cannot be recognized using traditional image recognition techniques, furthermore, even visually

Published in:

2011 41st IEEE International Symposium on Multiple-Valued Logic

Date of Conference:

23-25 May 2011