  Detection of surface defects is one of the most important issues in the field
of image processing and machine vision. In this article, a method for detecting
surface defects based on energy changes in co-occurrence matrices is presented.
The presented method consists of two stages of training and testing. In the
training phase, the co-occurrence matrix operator is first applied on healthy
images and then the amount of output energy is calculated. In the following,
according to the changes in the amount of energy, a suitable feature vector is
defined, and with the help of it, a suitable threshold for the health of the
images is obtained. Then, in the test phase, with the help of the calculated
quorum, the defective parts are distinguished from the healthy ones. In the
results section, the mentioned method has been applied on stone and ceramic
images and its detection accuracy has been calculated and compared with some
previous methods. Among the advantages of the presented method, we can mention
high accuracy, low calculations and compatibility with all types of levels due
to the use of the training stage. The proposed approach can be used in medical
applications to detect abnormalities such as diseases. So, the performance is
evaluated on 2d-hela dataset to classify cell phenotypes. The proposed approach
provides about 89.56 percent accuracy on 2d-hela.
