Summary: Abstract-- In this paper, we consider the problem of blurred
texture classification using a multilayer neural network based
on multi-valued neurons (MLMVN). We use the frequency
domain as a feature space. The low frequency part of the
Fourier phase spectrum of a blurred image remains almost
unaffected by blur. This means that phases corresponding to
the lowest frequencies can be used as features for classification.
MLMVN is the most suitable machine learning tool for solving
the problem, since it uses phases as inputs. MLMVN is based
on multi-valued neurons whose inputs and output are located
on the unit circle and therefore they are determined exactly by
phases. This determines a very important ability of MLMVN
and MVN to treat phases properly We employ in this paper a
slightly modified learning MLMVN rule and a modified
learning strategy, which extends margins between classes'
representatives used for the learning and the borders of classes.
This approach makes it possible to classify with 100% accuracy
even such heavily blurred textures where visual analysis and
classification are not possible at all.