Defect Detection on Texture using Statistical Approach
Abstract: In
this paper we
present several techniques
for detecting a
simple defect on the
texture. The simple defect is the defect that can be detected directly via
image histogram or via image
histogram of the
transformed original image
in the wavelet
space. In this
proposed method we used kernel density estimate instead of histogram for
presenting the distribution of the image gray levels. The simple defect can be
detected as the area in the tail of the image gray level distribution.
Therefore a threshold
in the left
or right (or
both) side(s) of
the gray level distribution is needed. This threshold
will indicate the defected area to the non defected area in the image
distribution. In this paper, we used three techniques to determine the
threshold poin. The first one, we used the concept of significance level in
statistical hypotheses, we assume that the probability of the defect gray level
lies in that level, e.g. alpha = 5%, the threshold point in this approach is
the point in the gray level (x-axis of the distribution) that makes the
probability of the gray level equal to alpha. The second approach, we used the
modified Otsu method, and the last one we used the Hill estimator. These
approaches will produce a rectilinear which covers the defected
area. The smallest
the rectilinear can
detect the defected
area the better
the performance of the
proposed method. In
this way of
measurement, Hill estimator
performs better than the other two proposed methods.
Author: Siana Halim
Kode Jurrnal: jptindustrigg150014
