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.